**Early Detection of Faults in Induction Motors**

Editors

**Daniel Morinigo-Sotelo Rene Romero-Troncoso Joan Pons-Llinares**

*Editors* Daniel Morinigo-Sotelo Institute of Advanced Manufacturing Technologies (ITAP), Research Group ADIRE-HspDigital Universidad de Valladolid Valladolid, Spain

Rene Romero-Troncoso HSPdigital–CA Mecatronica Universidad Autonoma ´ de Queretaro ´ San Juan del R´ıo, Mexico

Joan Pons-Llinares Departamento de Ingenier´ıa Electrica ´ Universidad Politecnica ´ de Valencia Valencia, Spain

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Energies* (ISSN 1996-1073) (available at: https://www.mdpi.com/journal/energies/special issues/ induction motors).

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## **Contents**

**Tomas A. Garcia-Calva, Daniel Morinigo-Sotelo, Vanessa Fernandez-Cavero, Arturo Garcia-Perez and Rene de J. Romero Troncoso** Early Detection of Broken Rotor Bars in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transients

Reprinted from: *Energies* **2021**, *14*, 1469, doi:10.3390/en14051469 ................... **173**

## **About the Editors**

#### **Daniel Morinigo-Sotelo**

Daniel Morinigo-Sotelo received the B.S.and Ph.D. degrees in electrical engineering from the University of Valladolid (UVA), Valladolid, Spain, in 1999 and 2006, respectively. From 2000 to 2015, he was a Research Collaborator on Electromagnetic Processing of Materials with the Light Alloys Division of CIDAUT Foundation. He is currently with the Research Group on Analysis and Diagnostics of Electrical Grids and Installations, which belongs to the ITAP Institute, UVA, and HSPdigital Research Group, Mexico. His research interests include fault detection and the ´ diagnostics of induction machines, power quality, and smart grids.

#### **Rene Romero-Troncoso**

Rene de Jesus Romero-Troncoso (Senior Member, IEEE) received his Ph.D. degree in mechatronics from the Autonomous University of Queretaro, Queretaro, Mexico, in 2004. He is currently a National Researcher level three with the Mexican Council of Science and Technology, CONACYT. He is a Head Professor with the Autonomous University of Queretaro, Queretaro, Mexico. He has been an advisor for more than 200 theses, the author of two books on digital systems (in Spanish), and coauthor of more than 130 technical papers published in international journals and conferences. His research interests include hardware signal processing and mechatronics. Dr. Romero-Troncoso was the recipient of the 2004 Asociacion Mexicana de Directivos de la ´ Investigacion Aplicada y el Desarrollo Tecnol ´ ogico Nacional Award on Innovation for his work in ´ applied mechatronics and 2005 the IEEE ReConFig Award for his work in digital systems. He is part of the Editorial Board of the Hindawi's *International Journal of Manufacturing Engineering*.

#### **Joan Pons-Llinares**

Joan Pons-Llinares received hisM.Sc. degree in industrial engineering and hisPh.D. degree in electrical engineering from the Universitat Politecnica de Val ´ encia (UPV), Val ´ encia, Spain, in 2007 and ´ 2013, respectively. He is currently an Associate Professor with the Electric Engineering Department, UPV. His research interests include time–frequency transforms, condition monitoring, and efficiency estimation of electrical machines.

## **Preface**

The idea of Industry 4.0 was first introduced at the Hanover Fair in 2011 and has since become a reality thanks to advancements in numerous fields, including digital technology, robotics, artificial intelligence, nanotechnology, biotechnology, the Internet of Things, 3D printing, and autonomous vehicles. The primary goal of Industry 4.0 is to improve industry asset management by combining all of these technologies. The design principles of Industry 4.0 were established in 2016 by Herman, Penntek, and Otto and include interoperability, information transparency, technical assistance, and decentralized decision-making.

This Special Issue of *Energies* focuses on the importance of detecting faults in induction motors, which are crucial industrial components and are also present in many different areas, such as transportation, service, and utilities. Although these machines are typically robust, they can fail and cause significant damage if not detected early. This can result in unplanned production stops, destroyed facilities, and service interruptions, which can be costly for companies. Therefore, there is a growing interest in developing early detection systems to prevent these failures from becoming catastrophic.

Information transparency and decentralized decision-making are two principles of Industry 4.0 that will shape the future of maintenance systems for these machines. This means that maintenance systems for electric motors must include more valuable context information beyond the raw data provided by sensors. In other words, maintenance systems must have instrumentation and monitoring systems and provide automatic device diagnosis and prognosis.

Traditionally, electrical machine condition monitoring has relied on analyzing stator current or motor vibrations. However, this Issue presents papers that propose using other signals, such as stray flux, sound, speed, and thermographs. The authors also present different techniques to process the signals of these physical variables (apFFT Time-shift phase difference spectrum, Prony methods, Min-Norm techniques, etc.) and obtain valuable information for automatic diagnosis based on models or machine-learning techniques.

### **Daniel Morinigo-Sotelo, Rene Romero-Troncoso, and Joan Pons-Llinares** *Editors*

### *Review* **Early Detection of Faults in Induction Motors—A Review**

**Tomas Garcia-Calva 1, Daniel Morinigo-Sotelo 2, Vanessa Fernandez-Cavero 3 and Rene Romero-Troncoso 4,\***

**Abstract:** There is an increasing interest in improving energy efficiency and reducing operational costs of induction motors in the industry. These costs can be significantly reduced, and the efficiency of the motor can be improved if the condition of the machine is monitored regularly and if monitoring techniques are able to detect failures at an incipient stage. An early fault detection makes the elimination of costly standstills, unscheduled downtime, unplanned breakdowns, and industrial injuries possible. Furthermore, maintaining a proper motor operation by reducing incipient failures can reduce motor losses and extend its operating life. There are many review papers in which analyses of fault detection techniques in induction motors can be found. However, all these reviewed techniques can detect failures only at developed or advanced stages. To our knowledge, no review exists that assesses works able to detect failures at incipient stages. This paper presents a review of techniques and methodologies that can detect faults at early stages. The review presents an analysis of the existing techniques focusing on the following principal motor components: stator, rotor, and rolling bearings. For steady-state and transient operating modes of the motor, the methodologies are discussed and recommendations for future research in this area are also presented.

**Keywords:** artificial intelligence; condition monitoring; early detection; fault diagnosis; fault severity; frequency analysis; incipient fault; induction motor; machine learning; signal processing

#### **1. Introduction**

Electric motor failures in industrial systems often result in unplanned downtime, loss of production, higher operating costs, and loss of profits [1]. The most common family of electric motors used in homes, businesses, and industry is the induction motor (IM) [2], for which there is a variety to choose from, depending on the power source, load requirements, mechanical interface, operating cost, energy efficiency, and reliability. IMs are often preferred over other kinds of motors since they are significantly less expensive, more robust, and capable of reliable operation in harsh ambient conditions, even in an explosive atmosphere. Induction motors, particularly those of the squirrel cage type, have been for almost a century the principal workhorse in industry [3]. Although IMs are more reliable than other types of motors, these machines are not exempt from developing faults in their structure or components, and these failures could lead to motor malfunction. Therefore, reliable condition monitoring for induction motors is of great value to avoid catastrophic unscheduled downtime [4]. An unexpected failure might lead to the loss of valuable human life or a costly standstill in industry, which needs to be prevented by precisely detecting the fault. The induction motor consists of many mechanical and electrical parts, such as a motor frame, stator windings, rotor cage, rolling bearings, fan, rotor shaft, among others. Despite induction motors are designed to be robust machines, they are exposed to external situations such as unstable supply voltage, unstable supply current sources, overloads, unbalanced loads, and electrical stresses. Due to the above-mentioned situations, damages

**Citation:** Garcia-Calva, T.; Morinigo-Sotelo, D.; Fernandez-Cavero, V.; Romero-Troncoso, R. Early Detection of Faults in Induction Motors—A Review. *Energies* **2022**, *15*, 7855. https://doi.org/10.3390/en15217855

Academic Editor: Sheldon Williamson

Received: 16 September 2022 Accepted: 20 October 2022 Published: 23 October 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

in operation and natural deterioration of the material parts (or manufacturing defects), the motor will eventually develop a faulty condition.

Different condition states of an IM component can be characterized as healthy or faulty condition. In the healthy condition, the internal components have no degradation, and the induction motor operates with maximum energy efficiency. There are three states that characterize a faulty condition in IM. The first one consists in an incipient fault, known as the early stage, where the degradation begins to develop in one or several of the internal components. Although the motor component has damage (i.e., partially broken rotor bars), the induction motor can continue to operate with no apparent symptoms. In the second faulty state, known as the developed fault, the damage on the motor component is advanced (i.e., one or more broken rotor bars). In this condition, the IM still operates; however, the damaged component severely affects the motor performance. Finally, the third faulty condition, known as catastrophic fault, occurs when the developed failure has propagated to other components, and the IM is no longer operating. Three stages of fault growth can be considered for an IM: the incipient fault with a steady propagation, the advanced fault with an accelerated propagation, and the catastrophic stage with a very accelerated propagation. The first stage is from the healthy state of the component until the very incipient fault state. This stage covers most of the portion of the IM component's useful life. After the incipient fault is present, the degradation propagates slowly until the developed fault state is reached. Once the IM component presents developed fault symptoms, the last stage is the accelerated propagation, where the fault grows rapidly until a breakdown. The early detection of IM faults is carried out before an internal component exhibits a developed fault stage. According to the literature, the tendency in industry and academia is to consider incipient fault of the rotor or bearing when there exists only a partial fracture of an internal component. Whereas for the stator, it considers incipient fault when there exists a short circuit among less than 3% of the total turns winding.

Some surveys [5–7] revealed that the occurrence rate of bearing faults can reach around 40% of the total failures in induction motors, while other studies indicated that this rate can be even higher for small motors. On the other hand, as per the study by the Institute of Electrical and Electronics Engineers (IEEE) and the Electric Power Research Institute (EPRI), faults that occur in the stator of an IM are 36% and 28%, respectively. Finally, it is reported in the literature that rotor faults are responsible for 8–10% of failures in induction motors [8]. Bearing defects usually lead to an increment in the sound and vibration levels as well as high temperatures. A severe damage can even provoke catastrophic failures (i.e., rotorstator rubbing, insulation damage). Moreover, stator failures during motor operation lead to reduced efficiency of the machines. Once begun, the stator fault provokes progressive degradation of insulating materials, ultimately leading to electrical breakdown. Despite rotor failures have a lower rate of occurrence compared to the above-mentioned ones, these faults are just as important because they can lead to shaft vibrations and winding damages, and thus bearing or stator failures. The existence of damages or anomalies at incipient stage implies additional losses in that part of the machine due to their improper operation. IM faults usually progress from incipient to a very advanced stage in a lapse of time, depending on the type of failure. Unless detected early enough, a motor failure may lead to costly standstill in industry or fatal consequences such as fire, explosion, and even loss of human lives. The constant need for reducing industrial injuries, unscheduled shutdowns, and operational costs has led to developing new techniques for early fault detection before they become prominent to cause machine failures. Some of the advantages of early fault detection in induction motors are as follows:

In the recent literature, there are some excellent reviews on fault detection techniques and their implementation in induction motors [12–16]; however, these reviews are limited to detecting developed or advanced faults. This paper presents a review of diagnosis techniques and methods for the detection of faults at incipient severity stages, due to its important role in the condition monitoring field, and decision-making in industrial maintenance activities. The existing literature on the subject is categorized into two approaches based on the operational mode of the IM: steady-state and transient regime. In the steady-state regime, the reported techniques apply analysis in the time-domain or frequency-domain to extract fault indicators and evaluate the motor condition. In the transient-state regime, the reported methods obtain fault indicators from time-frequency maps that allow evaluating the present state of fault indicators as well as their evolution over time. The organization of this paper is as follows. Section 2 presents the type of faults that induction motors develop in their main components and briefly describes the causes and consequences for each failure with special focus on early detection. A brief description of different signals used in the monitoring condition field for fault detection is presented in Section 3 including practical considerations for proper signal analysis and remarking advantages and limitations of each monitoring signal. In Section 4, a review of various techniques that are used for early fault detection in induction motors is presented; including those that are based on signal processing and knowledge-based. Section 5 is dedicated to the analysis of the different methodologies, whose strengths and weaknesses are described and discussed. According to the reviewed techniques, the conclusions are shown in Section 6. Lastly, in Section 7, directions and a future perspective are presented.

#### **2. Faults in Induction Motors**

Induction motor faults are commonly categorized as mechanical faults and electrical faults. Despite the existence of many fault classifications, this work categorizes motor failures according to the component that develops the fault for the sake of simplicity. The most common failures occur in three principal components of the rotatory machine. Figure 1 shows these main parts: the stator, the rotor, and rolling bearings.

**Figure 1.** Three fundamental components of an induction motor: stator, rotor, and rolling bearings.

#### *2.1. Stator Faults*

The stator consists of a laminated core, an outer frame, and insulated electrical windings. Its components are subjected to electrical and environmental stresses, which severely affect the stator condition leading to faults [17]. Stator faults (SF) can be categorized based on their localization as failure in the stator frame, fault in the stator winding, and failure in the laminations of the stator core. Among these, stator winding failures are the most severe faults and are often caused by failure of insulation of winding, which leads to local heating. If unnoticed, this local heating further damages the insulation of the stator winding until a catastrophic failure may occur. This fault is also known as the short circuit inter-turn fault. The appearance of stator faults depends on the size of the electrical machine [18]. According to [19], low-voltage IM stator faults account for only 9% of total failures. In medium-voltage IM, the percentage increases to 35–40%, whereas for high-voltage IM, it is more than 65%.

#### *2.2. Rotor Faults*

The rotor is the main driving shaft in induction motors through which the mechanical energy is transferred to the load. This component is placed inside the stator, and for a squirrel-cage type, it consists of the shaft, the aluminum or copper bars, and the end rings. Approximately 8 to 10% of all failures in IM are at the rotor. These faults can be classified as: broken rotor bars (BRB), cracked end-ring, and rotor eccentricities. Broken bars are caused by a combination of different stresses (mechanical, electrical, and thermal), manufacturing problems, dynamic stress from shaft torque, and fatigued mechanical parts [12]. This type of fault may not show any incipient symptoms, propagating to the next bars and leading to a sudden collapse of the rotor, producing damage in the stator and an abrupt interruption of the motor operation [20]. On the other hand, air-gap irregularities are produced by rotor eccentricities when the rotor axis of rotation does not coincide with stator geometrical axis. Manufacturing and constructive errors that generate a non-uniform air-gap or an incorrect positioning of the stator and rotor at the commissioning stage produce static eccentricity. When the center of the rotor is not at the center of rotation, then dynamic eccentricity is produced. The common causes of dynamic eccentricity are rotor shaft bending and bearing faults.

#### *2.3. Rolling Bearing Faults*

Rolling element bearings are the support of the shaft rotor in the induction motor in order to facilitate its rotation by reducing friction.  Bearing faults (BF) are classified as localized failures and distributed faults (roughness or non-cyclic) [7]. Distributed defects affect a whole region and their mathematical modeling is very difficult. In contrast, localized failures are single-point defects and can be classified according to the affected element: inner raceway defect, outer raceway defect, ball defect, and cage failure. Bearing wear can be caused by a wide variety of reasons, such as excessive or deficient lubrication (due to inadequate viscosity, excess or lack of grease, lubricant contamination, etc.), circulation of bearing currents (in power converter-fed motors), brinelling (due to punctual overloads, severe impacts), etc.

#### *2.4. Early Detection of Faults*

Induction motors are symmetric machines and the occurrence of any type of fault is linked to the harmonic content of its monitored signals. The existence of a fault in IM results in the appearance of specific frequency signatures. In general, fault detection techniques are based on the magnitude evaluation of these signatures. When there is an anomaly in the mechanical structure of the rolling bearing, characteristic frequencies emerge in the vibration spectrum as a consequence of the asymmetry. There are two bearing defects that are analyzed in the literature from the severity level point of view, the outer race defect and the inner race defect. The related frequencies are shown below [21].

$$\text{Inner recovery}: \ f\_i = \frac{N\_b}{2} f\_r \left( 1 + \frac{D\_b}{D\_c} \cos \beta \right), \tag{1}$$

$$\text{Outer recovery}: f\_o = \frac{N\_b}{2} f\_r \left( 1 - \frac{D\_b}{D\_c} \cos \beta \right), \tag{2}$$

where *fr* is the rotational frequency, *Nb* is the number of balls in the bearing, *Dc* is the pitch or cage diameter, *Db* the diameter of the balls, and *β* is the contact angle between a ball and the raceway. When a stator fault occurs, the current through the shorted winding affects the magnetic field and is reflected in the axial flux as follows [22].

$$\text{Stator fault}: \ f\_{ss} = f\_s \left[ \left( k \pm n \frac{1-s}{p} \right) \right] \tag{3}$$

where *k* is the order of the time harmonic, *n* the order of the shorted coil space harmonic, *s* the machine slip, *p* the number of pole pairs, and *fs* the supply frequency. The damage of broken bars in the rotor produces additional frequencies in the current spectrum. These signatures are characterized by [23]:

$$\text{Broken rotor bars}: \ f\_{brb} = f\_s(1 \pm 2s) \tag{4}$$

Additionally, any fluctuation in the load torque will produce oscillations in the stator currents at frequencies of

$$\text{Load oscillation}: \ f\_{load} = f\_s \pm mf\_r = f\_s \left[ 1 \pm m \left( \frac{1-s}{p} \right) \right] \tag{5}$$

where *m* is the order of the harmonic. Since the same fault harmonic is given by BRB, a low-frequency load-oscillation results in stator currents that can overlap those produced by the BRB fault [24].

The magnitude of each of these fault frequencies is directly related to the severity level of the fault. The higher the severity level, the higher the magnitude of the spectral component. Each failure type has a different evolution over time since the degradation of the component depends on its construction and the material from which it is made. Figure 2 illustrates the degradation of the motor components and its severity levels: bearing failure (Figure 2a), stator failure (Figure 2b), and rotor fault (Figure 2c). The tendency in industry and academia is to be able to make an early detection of the motor component degradation. For bearing failure, the early detection is considered when the diameter of the crack (*λ*) is less than 1/8 the diameter of the bearing ball (Θ). For broken rotor bars, early detection is considered when one bar is partially broken and the depth of the breakage (*ρ*) is less than the total length of the bar (*l*). Whereas for stator faults, it is considered that an early fault occurs when there is a short circuit between less than 1/30 times the total turns of the winding (Φ). Figure 2d–f illustrates the relationships of the fault with the construction of the motor components. As component degradation is very low at an early stage, the magnitude of fault signatures is also very low. Therefore, a condition diagnosis of the motor component when the degradation is incipient presents a challenge in the detection, identification, and evaluation of failure indicators.

The most important characteristic of any condition monitoring scheme is its quickness of detection. Different types of faults usually progress from incipient to a very advanced stage in a different manner, as is shown in Figure 2. This work only considers fault detection at an early stage. For the detection of early rotor failure, only partially broken rotor bars are taken into account, whereas fully broken rotor bars are considered a developed fault. This is because once a bar is completely fractured, the failure spreads rapidly to adjacent bars and subsequently damages the stator winding causing irreparable damage. In the case of bearing faults, the early fault condition is considered when the diameter of the fracture in the inner or the outer race is less than 12.5% of the diameter of the bearing ball, since from then on, the bearing stroke undergoes great alterations and alters the rotor symmetry, making the failure to be considered as developed or advanced. Finally, the stator failure is considered in an early condition before exceeding 3.33% of the turns in the stator phase. Once the short circuit begins, it propagates rapidly in a very short time. Unless detected early enough, it might lead to catastrophic consequences. Faults detected at advanced stages are far more likely to cause unplanned breakdowns in the line production than those detected while the failure is still at an early stage. Techniques that can detect faults at an early stage are very desirable for the possibility of correcting the faulty condition entirely with low impact to the production line. For early detection to be an effective and practical approach, techniques must satisfy three basic requirements. First, the detection analysis should be able to distinguish faulty IM from healthy IM cases with a high degree of accuracy, showing both low rates of false-positives and false negatives. Second, the detection should be possible before the fault progresses to a developed stage, when the propagation is accelerated, and preventive actions are less effective. Lastly, the

diagnosis methodology should allow the assessment of the IM condition when the motor is fed by inverters. It must be noticed that inverters induce several spectral components to the voltage, current, and vibration signals, which overlap with the fault-related spectral signatures; moreover, the magnitude of the fault-related components are very close to the noise floor, making the evaluation of the fault severity difficult.

**Figure 2.** Degradation stages of induction motor components: (**a**) rolling bearing degradation, (**b**) stator winding degradation, (**c**) rotor bar degradation, (**d**) rolling bearing schematic, (**e**) stator winding schematic, and (**f**) rotor bars schematic.

#### **3. Monitoring Signals for Fault Detection**

When IMs are running, each type of fault generates characteristic features in the machine's behavior. Fault detection techniques can be based on the analysis of vibrations, stator current, shaft speed, acoustic emissions, voltage, internal air-gap flux, external stray flux, electric power, and temperature (usually trough infrared thermography).

#### *3.1. Vibration Monitoring*

Among the many condition monitoring techniques, vibration-based methods are the most widely preferred owing to their reliability, non-intrusiveness, and easy measurability. Vibration monitoring has been used for decades and utilized to detect mechanical faults in IM. In working mode, radial magnetic forces are produced between the rotor and stator surfaces and are proportional to the square of the flux density. These forces result in stator core, winding, and motor frame vibrations. As faults associated with the rotor, stator, and rolling bearings alter the machine symmetry, vibration signals which are a function of the symmetrical air-gap and symmetrical components will also change [25]. Most vibration measurements usually use sensors of vibration-acceleration that work based on the piezoelectric effect, whose output voltage is proportional to the force applied to the sensor [26]. The vibration signals need to be processed in order to extract the fundamental

components and to filter out nonlinear effects due to the cover frame and noise from the environment.

#### *3.2. Stator Current Monitoring*

Even though vibration signals have been widely utilized, most of the work in the last decade has been directed toward stator current monitoring also known as motor current signature analysis (MCSA) [27]. The stator current monitoring can provide unique fault patterns for the effective detection of mainly electrical faults, i.e., the stator winding fault, broken rotor bars fault, phase unbalance, and single phasing [28]. Motor current monitoring provides a non-intrusive way to continuously monitor the health condition with the aim of using non-invasive sensors and possibly already existing in the drive for motor control.

#### *3.3. Magnetic Flux Monitoring*

An alternate approach based on magnetic flux monitoring has received the attention of many researchers and motor manufacturers during the last years. The great improvements and reduction in the costs and dimensions of the required transducers, the development of advanced signal-processing tools that are suitable for magnetic flux analysis, along with other inherent advantages provided by this technology, are important aspects that have permitted the proliferation of flux-based methods [29]. The magnetic flux in IM is broadly classified as internal magnetic flux and external stray magnetic flux. However, using magnetic flux as monitoring signal for fault detection is not new and there are works carried out decades earlier [30].

#### *3.4. Others*

Apart from locating specific features in the above-mentioned signals, other physical magnitudes, such as rotor position, rotor speed, torque, acoustic emissions, electric power, and temperature have been investigated in the recent years by researchers in the field. Sometimes, the combination of several monitoring signals is used by fault detection techniques to improve the detection rate. There are also other detection methods being developed on modeling and control techniques, such as state observers [31], state estimators [32], signal injection, and parameter estimators.

Despite the relevant advances obtained with the vast diversity of proposed approaches in the fault detection field, the effectiveness of the techniques usually depends on the induction motor size, loading, state operation (steady or transient), supply type, control mode, type of the fault, and to a great extent, on the type of the monitoring signal. Table 1 summarizes some of the main advantages and drawbacks of the most common monitoring signals.

**Table 1.** Main advantages and drawbacks of common monitoring physical magnitudes.

#### **4. Review of Techniques and Methodologies**

In general, early fault detection techniques based on signal analysis relies on: (1) acquiring one or various physical magnitudes of the induction motor, (2) processing the measured magnitudes (signals) with suitable tools to extract fault patterns, and (3) analyzing the patterns to determine the fault severity. According to the reviewed technical literature, two main approaches for early fault detection can be determined based on the IM operation mode: the steady-state analysis and transient-state analysis. The techniques employed in these approaches depend on the stationarity or non-stationarity of the monitored signals.

#### *4.1. Early Fault Detection via Steady-State Analysis*

When an induction motor operates at a constant speed, measured magnitudes from the machine can be described by periodic signals. Thus, in the literature, the best-known techniques for processing these signals belong to the frequency-domain analysis.

Although most IMs in the industry are fed directly from the grid, in the recent years, the use of inverters has become more widespread due to their capabilities in speed control and energy efficiency advantages. Nevertheless, their use also affects fault detection in IMs due to the higher noise and rich harmonic content they cause in the measured signals for condition monitoring. For these reasons, it is important to separate the analysis of early fault detection methods according to the motor power supply.

#### 4.1.1. Direct Line-Fed Induction Motors

Condition monitoring methods for the early detection of induction motor faults based on signal spectral analysis are found in the literature. In [33], the authors presented a methodology for incipient broken rotor bar detection based on the spectral analysis of vibration signals with the FFT. The proposed approach is based on sparse signal representations [34] and the use of dictionaries trained with sets of signals with the fault to be detected. The incipient fault is simulated by drilling only 5% of the length of a rotor bar, and high detection accuracy is achieved even in unloaded motor operation but only for a line-fed induction motor.

As the authors of [35] pointed out, online detection of low-severity stator interturn faults is one of the most challenging electrical machine defects to detect. If this fault develops undetected, it can lead to a ground fault and complete motor burning. The authors used a modified induction motor that allows them to simulate very low severity interturn faults: 0.25%, 0.5%, and 0.75%. They proved that conventional monitoring methods, such as MCSA, Park's vector approach and negative sequence current analysis, are unreliable and insensitive to these very low-severity interturn faults. However, they were able to detect these faults by using three different coils placed in different motor locations to collect the motor stray flux. The Park vector of these three signals is calculated, and its modulus is analyzed with the FFT.

Recent condition monitoring approaches have benefited from advances in computational technologies that allow the combination of signal processing methods (time and frequency domain) with knowledge-based techniques (KB) such as machine learning (ML), genetic algorithms (GA), artificial intelligence (AI), surface learning and deep learning (DL). For instance, reference [36] dealt with the detection of partially broken rotor bars. It proposed a hybrid approach combining electrical-synchronous averaging (ETSA), Discrete Wavelet transform (DWT), and fuzzy logic algorithms. The first two techniques are used to identify a frequency band with fault-related components in the stator current, and the third is a classifier to assess the severity of the fault. It is worth noting that this methodology can detect a hole of only 2 mm in a 36 mm bar, even under no-load conditions.

In the paper [37], hybrid DL architectures were explored to solve the problem of classifying interturn faults at an incipient stage. This detection method uses a hybrid architecture based on a one-dimensional Convolutional Neural Network (1DCNN), long short-term memory, and gated recurrent unit. This approach can distinguish this fault from other conditions, such as voltage imbalance and load variations.

Reference [38] also showed very good results in detecting interturn faults at an early stage. The authors compared two different ANN-based approaches: (1) the first one is based on a multi-agent system with a classification behavior; (2) the second one uses a neural estimator. The input data of both methods are the analysis of the stator current in the time domain, which differentiates them from other approaches based on frequency-based signal processing. Their approach has been tested by simulating short circuits covering 1 to 10 percent of the stator winding and with a wide range of motor power ratings and voltage supplies. Reference [39] made another contribution to the same topic as the previous one. Their proposal is based on analyzing the negative sequence of the stator current to detect motor asymmetries produced by this type of fault. The results are based on simulation and corroborated by experimental measurements under varying motor load conditions. Most of these knowledge-based (KB) works achieve good detection accuracy of over 95%. However, it is important to note that these KB approaches require a large amount of data (from both the healthy and faulty induction motors), so they have limitations for generalization and are techniques with low scalability.

The detection of bearing failures has also attracted many researchers, and numerous papers have been published. However, few of them deal with their early detection or the diagnosis of these faults at an early stage before they develop into catastrophic breakdowns. The vast majority of these works is based on signal processing, knowledge-based techniques or a combination of both.

In [40], the processed signal is the stator current, which allows the detection of incipient cage and outer race-bearing faults in a 2.2 kW line-fed induction motor. The authors proposed a spectral frequency subtraction using several wavelet transforms (DWT, stationarity wavelet transform (SWT), and wavelet packet decomposition (WDP)) to suppress the dominant components in the stator current spectrum. This suppression makes it possible to observe fault-related frequency components whose amplitude is very small, especially at an early fault stage. Due to the impossibility of installing vibration sensors in many industrial applications, the authors in [41] also proposed using two stator current signals for early bearing damage detection. Their interesting contribution pre-processes these two signals with fractional B-spline wavelet transforms for denoising them. Next, the overlapping group shrinkage (OGS) algorithm reconstructs two signals that will be used as the input to a convolutional neural network (CNN) and a long short-term memory (LSTM) algorithm for feature extraction. Next, information fusion and fuzzy c-means algorithms perform the fault diagnosis. Remarkably, this methodology is based on unlabeled learning, and it is tested in an actual industrial application with an induction motor driving a centrifugal pump.

#### 4.1.2. Inverter-Fed Induction Motors

Even though much research has been done on incipient fault diagnosis in induction motors, all previous works still deal with line-fed motors under stationary conditions. Recently, in industry, it is more common to see IM fed by power electronic converters (also known as voltage source inverters) [42]. Furthermore, the stationary operation is quite unusual in the industrial environment due to voltage variations, speed oscillations, and load changes [43]. In this context, some techniques have been proposed and showed very good results for early fault detection in inverter-fed IM and some of them under nonstationary conditions. In [44], a combination of two-level hybrid hierarchical CNN with SVM was proposed for incipient interturn fault diagnosis. The authors showed that their proposal is fast and has significant performance improvement in accuracy in comparison with other proposals. The authors of [45] concentrated on the incipient inter-turn short circuit detection and estimation of its severity. Moreover, they studied the effect of load oscillations on the recognition of fault patterns in the stator current. Another combination of SP and KB techniques can be found in [46], the authors described a methodology combining the DWT for multi-resolution analysis, statistical features, and ML to detect incipient short circuits using axial leakage flux signals.

Several authors targeted the detection of one or more BRB in IMs controlled by electronic converters. Besides the fact that many of those works established a successful detection, other works improved the techniques to extract more incipient BRB fault features from the measurements. Interestingly, in [47], the authors presented a novel supervised classification approach for BRB fault diagnosis based on adaptive boosting algorithm with an optimized sampling method that deals with an imbalanced experimental dataset. Experimental results of the proposal provide accurate diagnosis of different intermediate severities before a full BRB. Another work that investigates various severity levels was presented in [48], where a combined approach with the fast Fourier transform (FFT) and the multiple signal classification (MUSIC) algorithm is proposed. This study exposes that incipient rotor faults in a squirrel-cage rotor, prior to the complete breaking of a rotor bar, are better identified when IMs are feed by some inverters than others. In addition, to detect incipient BRB, the authors of [49] proposed to use robust statistical techniques that are commonly applied in quality control applications. In this study, the proposal can detect partial bar breaks of 6.4 mm, 11.7 mm, and 17 mm (a full-broken bar).

Otherwise, different types of diagnosis methodologies have also developed for bearing failures in inverter-fed induction motors. The investigation in [50] presented a technique to attenuate multiple dominant harmonics with the aim to reduce spectral leakage, expose minute fault components, and improve the amplitude estimation of fault-related harmonics. Experimental outcomes of the methodology prove that the algorithm used for the spectral estimation of the vibration signal is adequate for an early determination of inverter-fed IM faults (inner raceway, outer raceway, cage train, rolling element, and in a single bar of the rotor). Another interesting technique for bearing failure recognition was presented in [51]. Its main contribution lies in the proposal of a condition monitoring strategy that is focused on the analysis and identification of five different fault severities of the outer race bearing (drill bits with diameters of 1 to 5 mm). The proposed approach is supported by fusing information from different sensors and the application of ML and AI. Apart from KB methods, reference [52] proposed a new approach based on a dynamic model in *d* − *q* coordinate systems and analyzed BRB and turn-to-turn short fault. This approach uses a residual technique between model and measured signals. Experimental analyses show that the designed detection and isolation scheme provides high sensitivity and accurate isolation to incipient winding faults. To suppress the impact of the motor fault, an interesting control method was proposed in [53]. This control method includes a monitoring technique that can detect faults occurring due to stator winding short circuit at an incipient stage by means of a harmonic analysis of the magnetic air-gap flux. Methods based on steady-state analysis such as the above-mentioned are noteworthy in motor fault detection. However, these methods have severe limitations and may lead to false positives or false negatives. Table 2 shows a performance comparison of the above-mentioned applied methodologies.

**Table 2.** Comparison of early fault detection methodologies applied on IM under steady regime.

**Table 2.** *Cont.*

#### *4.2. Early Fault Detection via Transient-State Analysis*

On the other hand, the analysis of IM data with non-stationary behavior mostly relies on time-frequency transforms computed from signals that are measured when motors are running under transient regimes. The most common time-frequency decompositions are the short-time Fourier transform (STFT) and the wavelet transforms (WT). The use of a combination of one variant of wavelet transform, the recursive wavelet transform, and a widely used tool in quality control, the statistical process control was presented in [54] to detect incipient stages of rotor fault. The methodology offers high accuracy in broken bar detection, starting from a deep hole of 3 mm in one bar to 2 fully broken bars. The detection was implemented during a steady-state operation mode and for short transient events of the motor. Another methodology using the wavelet transform has been reported in [55]. The fault diagnosis system is based on an empirical neuro-predictor and the application of wavelet analysis to residual signals between the model and the measured physical magnitudes. The method reports effective accuracy in detecting the most widely encountered electrical and mechanical faults. The motor anomalies consist of variations in the balance of the electric power supply and the driven mechanical load level when the IM experiences short transients as the load is varied from 0% up to 120% of the rated load. The investigation carried out in [56] examined the impact of incipient rotor faults on the shaft speed of an inverter-fed induction motor. This work used a tachogenerator to measure the rotor speed and applying a high-resolution spectral analysis (MUSIC algorithm) which detects and quantifies the fault severity in the time-frequency domain. This methodology can identify 5 health condition levels (healthy, 1 4 BRB, 1 2 BRB, 3 4 BRB, and 1 BRB deep hole of 13 mm) during startup transients of 10 s. In [57], the stator current of a DOL-fed motor starting was used to extract statistical features, and using homogeneity as a classification index. This methodology can identify and classify differences between distinct fault severity conditions of the rotor bars (e.g., healthy, 1 2 BRB, 1 BRB, and 2 BRB). The low computational complexity of the homogeneity index makes the method suitable for hardware implementation. The authors of [58] provided interesting time-frequency results for detecting various motor conditions such as stator winding interturn shorts, and phase to ground faults. In this work, the Stockwell transform (STW) was used to analyze the starting current of a DOL startup transient, the resulting time-frequency matrix was used to extract fault features and fed two different support vector machine (SVM) models. An average classification accuracy of 96% was achieved for both types o faults. Other researchers proposed in [59] a fault diagnosis technique based on the acquisition of signals from multiple sensors in order to assess the occurrence of single, combined, and simultaneous fault conditions in an induction motor. The proposal performs principal component analysis (PCA) to each signal, then joined as input to a decision tree method. The considered early fault stage was a rough hole of 2 mm diameter, at a depth of 14 mm, into the rotor bars of a DOL-fed motor. As another example of the transient analysis, in [60], the tooth-FFT algorithm was introduced to track time-varying frequency components. Half and full broken bars were considered; experimental results obtained a

percentage of detection of 97.35% for all motor conditions. In Table 3, a comparison of the applied methodologies is presented.

**Table 3.** Comparison of early fault detection methodologies applied on IM under transient regime.

#### **5. Discussion**

According to the literature, three monitored signals are principally used for early fault detection in IM: mechanical vibrations, stator current, and magnetic flux. The vibration signal is widely used because it is sensitive to internal asymmetries in the machine during its operation. Despite this advantage, it is difficult to determine specifically the internal fault that produces the asymmetry. Furthermore, used sensors are sensitive to external vibrations also; thus, it is common that vibration analysis suffers from external interference. Important to note that monitoring vibration signals requires a short sampling period at the acquisition stage due to fault patterns appearing in the high-frequency band. On the other hand, the stator current contains specific fault patterns for each type of fault and the measurement is not very sensitive to external interference. Furthermore, most of the spectral patterns related to a fault type are generated in the low-frequency band of the spectral content. Therefore, a short sampling period in the acquisition stage is not necessary. Despite the benefits of current monitoring, when the induction motor is powered by an electronic converter, additional frequency components are induced in the current spectrum, thereby adding many spectral components foreign to the MI behavior that can alter the magnitude of the fault indicators.

Some works use magnetic flux signals. This type of magnitude is spectrally dense, so it has many frequency components interacting with each other, which makes it difficult to evaluate the magnitude of one spectral component without interference from another. One of the advantages presented by the authors when monitoring this signal is that the magnetic flux is not sensitive to external mechanical vibrations and depending on the location of the sensor; the flux signal makes it possible to locate faults in specific places of the motor.

Most of the reviewed works focus on analyzing signals from IM operating at steady state. This analysis has many advantages compared to the transient-state analysis because in the steady-state analysis, several fault indicators can be extracted by classic signal processing techniques. On the contrary, the analysis of non-stationary signals requires more complex tools and advanced signal processing techniques. Despite the complexity of the analysis, the study of IM operating in transients permits locating and tracking the behavior of specific fault indicators for each internal motor component. This allows more accurate fault identification and severity diagnosis compared to the analysis of an IM operating at steady-state.

In the literature, it is possible to find three groups of techniques that are mainly used for early fault detection: classical, modern, and heuristic. In Table 4, a list of the applied methodologies is presented. First, in the classical techniques, statistical tools have been used mainly to extract fault features from the behavior of signals in the time-domain. Meanwhile, frequency-domain tools have also been used to extract fault components from the spectral content of the analyzed signal, the best known is the Fourier transform. Although these classical techniques have worked to detect incipient faults in IM operating at steady-state, this group of techniques is not appropriate for monitoring time-varying systems. Some authors used modern techniques, which allow the analysis of physical magnitudes in a simultaneous time-frequency domain. The tools used for a time-frequency analysis are the STFT, the wavelet transform, the Winner–Ville transform, the Hilbert–Huang transform, and high-resolution decompositions (short-time MUSIC), among others. These modern techniques have positioned as great alternatives in the field to reduce diagnostic errors that classic techniques have. Despite the advantages of this type of techniques, they require a greater computational burden and a higher level of interpretation of results than classical techniques. In most recent works, the application of heuristic methodologies such as GA, pattern recognition, ML and AI techniques can be found. These works report high levels of accuracy in the detection of some faults at incipient stage. Despite the good results reported, these methodologies require a very large and diverse database.

**Table 4.** List of the different applied techniques.

**Table 4.** *Cont.*

#### **6. Conclusions**

This paper has reviewed the most recent contributions related to the early fault detection in induction motors. These contributions are classified into two main groups according to the operational mode of the motor: steady-state and transient-state. In this paper, it is shown that most of the research work is focused on the steady-state analysis. Despite the high-level of accuracy reported in the fault severity techniques based on steady-state analysis, these proposals still suffer for diagnostic errors. This work also presents reported algorithms based on different type of monitoring signals used for fault detection, and some characteristics of each measured magnitude. According to the developed review, it can be concluded that the techniques most used for fault detection at incipient stages are heuristic methods (knowledge-based), and sometimes a combination of signal processing methods and KB. The major problems with heuristic methods are the required computational resources and the diverse and large amount of data. Despite the amount of work carried out in detection of incipient faults of IM, just few works analyze transients and just some of them analyze inverter-fed IM transients. Regarding the fault type, most of the research work is focus on the detection of partially broken rotor bars.

#### **7. Future Perspective**

Despite constant research activity in the fault detection field, it can be observed that incipient fault detection and severity fault evaluation in IM is still an open challenge. As most of the existing research has been focused on the detection of incipient faults in IM operating under the steady regime, the study of IM operating under non-stationary regime is a natural trend. More importantly, as the use of inverters is increasing in the industry, an approach for future research is the development of new techniques that can diagnose incipient faults in the inverter-fed IM under transient operation. In addition, although some methods to separate load-oscillations from BRB signatures have been proposed [61–66], the effectiveness of early fault detection methods against this kind of external oscillation still needs to be validated. Several techniques have been applied to current and vibration signals; therefore, the reliability of other monitoring signals it could be explored. In order to obtain robust and practical solutions, various corner cases and environmental conditions should also be analyzed, such as the effects of combined multiple faults, realistic degradation, and industrial measurement inference. In in this paper, it is shown how KB techniques have been an increasing activity over recent years, opening new opportunities in which the application of these new techniques and the combination with modern SP can bring interesting advantages. Finally, new indicators are required to know the severity stage of a fault and to extract its features for improving the classification and the remaining useful life of the internal element. This necessity will encourage the development of new techniques able to filter out external interferences and, at the same time, quantify the fault indicator features. In addition, new techniques to be proposed should include the benefits of the existing works in the developed fault detection field, such as reliability, feasibility

for implementation (low computational burden), portability, online detection, detection of multiple and combined faults, and so on.

**Author Contributions:** T.G.-C. performed the investigation of several of the revised works in the state of the art, performed analysis, and wrote a part of the paper; D.M.-S. performed the investigation of several of the revised works in the state of the art and wrote a part of the paper; V.F.-C. performed the investigation of several of the revised works in the state of the art and wrote a part of the paper; R.R.-T. conceived and developed the idea for this research, performed analysis, and wrote some of the sections. All authors have read an agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** No new data were generated.

**Conflicts of Interest:** The authors declare no conflicts of interest.

#### **Nomenclature**

The following abbreviations are used in this manuscript:

#### **References**

## *Article* **Stator ITSC Fault Diagnosis of EMU Asynchronous Traction Motor Based on apFFT Time-Shift Phase Difference Spectrum Correction and SVM**

**Jie Ma 1,2, Xiaodong Liu 1, Jisheng Hu 1, Jiyou Fei 1,\*, Geng Zhao 1 and Zhonghuan Zhu 3**

**Abstract:** EMU (electric multiple unit) traction motors are powered by converters whose output voltage increases the voltage stress borne by the insulation system, making the ITSC (inter-turn short-circuit) fault more prominent. An index based on short-circuit thermal power is proposed in the article to evaluate the non-metallic ITSC faults extent. The apFFT (all-phase FFT) time-shift phase difference correction with double Hanning windows is used to calculate fault features to train the SVM (support vector machine) fault diagnosis model whose hyper-parameters C and g are optimized using grid search methods. The experimental verification was carried out on the EMU electric traction simulation experimental platform. According to the fault extent index proposed in this article, the experimental samples were divided into three categories, normal, incipient and serious fault samples. The ITSC fault diagnosis accuracy was 100% on the training dataset and 93.33% on the test dataset. There was no misclassification between normal and serious ITSC fault samples.

**Keywords:** ITSC fault; traction motor; fault diagnosis; apFFT; SVM

**Citation:** Ma, J.; Liu, X.; Hu, J.; Fei, J.; Zhao, G.; Zhu, Z. Stator ITSC Fault Diagnosis of EMU Asynchronous Traction Motor Based on apFFT Time-Shift Phase Difference Spectrum Correction and SVM. *Energies* **2023**, *16*, 5612. https://doi.org/10.3390/ en16155612

Academic Editors: Daniel Morinigo-Sotelo, Joan Pons-Llinares and Rene Romero-Troncoso

Received: 26 April 2023 Revised: 14 July 2023 Accepted: 17 July 2023 Published: 26 July 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**1. Introduction**

The AC–DC–AC transmission mode is used in modern EMU traction systems, and three-phase AC squirrel-cage asynchronous motors are used as traction motors [1]. Affected by mechanical stress, thermal stress, and electrical stress, EMU traction motors are prone to failure [2,3]. The fault types of three-phase AC asynchronous squirrel-cage motors in industrial applications mainly include stator insulation faults (37%), rotor broken bar faults (12%), bearing faults (41%), and other faults (10%) [4]. The high voltage stress generated by the inverter PWM (pulse width modulation) voltage accelerates the degradation of the traction motor insulation system [5–8]. The inter-turn insulation is the weakest part of the asynchronous traction motor insulation system [9]. When an ITSC fault occurs, an interturn current circulates between the short-circuit turns, quickly producing a large amount of heat [10], which weakens the insulation of the motor and results in inter-phase or ground short-circuit faults [11]. Timely maintenance can prevent the ITSC fault's further expansion and significantly reduce the maintenance cost. Since the ITSC fault of the asynchronous traction motor is more hidden than the main insulation system fault, it is more difficult to detect the incipient ITSC fault [12,13].

The ITSC fault diagnosis of three-phase asynchronous motors mainly includes modelbased, signal process–based, and artificial intelligence–based diagnosis methods [14–16]. An accurate motor ITSC fault model is needed for model-based ITSC fault diagnosis. Modelbased methods mainly include the parameter estimation method and residual estimation method. With the parameter estimation method, the model parameters related to the ITSC

fault are estimated [17–19]. Based on the three-phase asynchronous motor ITSC fault model under the dq axis, the particle filter algorithm is used to estimate multiple parameters to detect the stator ITSC fault and assess the motor insulation residual life [20]. With this method, real-time and non-intrusive diagnosis can be easily achieved by just measuring motor phase voltage and phase current. A healthy motor model is necessary for the residual estimation method, which takes the detectable variables related to the ITSC fault as state variables. It uses the difference between the state variables of the healthy motor estimated by the model and the measured variables as the residual to detect the ITSC fault [21,22]. The three-phase current is taken as the state variable, and the high-order sliding mode observer is used to observe the three-phase current of the healthy motor [23]. The residual of the observed and measured values is taken as the index for the ITSC fault. The modelbased ITSC fault diagnosis method can be easily affected by working conditions. For example, when the method of estimating stator resistance is used to diagnose ITSC faults, the diagnostic results are highly influenced by temperature and frequency (skin effect).

The diagnosis method for the ITSC fault based on signal processing is mainly based on the electrical, magnetic, thermal, vibration, and acoustic signals. The stator ITSC fault is diagnosed by analyzing and processing the above signals in the time, frequency, or time–frequency domains [24–26]. The voltage or current signals can be used to realize non-intrusive diagnosis [27], saving costs without the need for installing extra sensors. For the steady operation state, the FFT algorithm is generally used to calculate specific frequency components of the current or other signals to detect the ITSC fault [28]. With the continuous development of new signal processing methods, time–frequency analysis methods, such as wavelet transform, WVD (Wigner–Ville distribution), and HHT (Hilbert– Huang transform), are also applied to the motors' fault diagnosis [29,30]. The discrete wavelet is used to decompose the stator current, and the maximum norm of the detail coefficient is used to detect the incipient ITSC fault [31].

Shallow machine learning and deep learning methods are also applied to motor stator ITSC fault diagnosis [32–35]. The ITSC fault diagnosis method based on shallow machine learning is generally divided into three stages: data preparation, feature extraction, and model training. Particle swarm optimization and principal component analysis can be used for feature extraction. The BP neural network and SVM models can be used as diagnosis models. The BP neural network is trained based on the phase difference of the threephase stator current, which can detect and locate the stator ITSC fault [36]. When a deep learning network is adopted, such as a convolution neural network, it is unnecessary to extract features artificially because the deep learning network automatically extracts them. The instantaneous value of the three-phase current is taken as the feature, the convolution neural network is taken as the diagnosis model, and the trained convolution neural network can accurately detect the ITSC fault of a three-phase asynchronous motor [37].

Research on the diagnosis method for ITSC fault of asynchronous motors stator has achieved many positive results, but the diagnosis of EMU traction motor stator ITSC fault has special needs. Firstly, in previous studies, the extent of an AC motor ITSC fault is generally evaluated based on the number of short-circuit turns when the inter-turn resistance is fixed. In the non-metallic short circuit, the resistance between short-circuit turns is directly related to the extent of the motor damage caused by the fault. Secondly, most of the previous studies are carried out in the condition of non-variable frequency speed regulation, and the steady speed of the motor is fixed. The traction motor operates stably at different speeds according to operating conditions. Finally, the traction motor of the EMU adopts vector control or direct torque control based on the current closed loop. The fundamental frequency of voltage and current signals cannot be directly obtained, and a spectral correction method is needed to achieve a more accurate fundamental frequency.

The article is mainly divided into five parts. After the introduction, it introduces the measurement method for the traction motor's ZSVC (zero-sequence voltage component) and the apFFT time-shift phase difference correction method. This method is used to calculate the traction motor's ZSVC fundamental frequency, the fundamental component

amplitudes of the ZSVC and the three-phase current in a steady state. In the third part, an ITSC fault extent index related to the number of short-circuit turns and the inter-turn resistance is proposed. The index is based on the thermal power of the circulating current between short-circuit turns. In addition, the SVM and the hyper-parameter optimization method are introduced in this part. In the following section, SVM is used to diagnose the ITSC fault. The fourth part is the experimental part. The EMU electric traction simulation experimental platform is used to simulate the steady-state operation of the EMU. According to the fault extent index proposed in this article, the experimental samples are divided into normal, incipient, and serious fault samples, and the ITSC fault diagnosis model is trained and tested. The last part summarizes all the research contents and puts forward the follow-up work.

#### **2. Calculation of Signals' Fundamental Components**

#### *2.1. ZSVC Measurement Method*

The traction motor's current is measured for speed and torque control during the operation, and only the ZSVC needs to be measured additionally. The ZSVC of the threephase asynchronous motor can effectively monitor the stator ITSC fault [38,39]. The measurement circuit is relatively simple, and installing sensors on the motor body is unnecessary. According to the ZSVC definition of a three-phase asynchronous motor [40,41], as shown in Formula (1), three voltage sensors are needed when measuring the threephase voltage.

$$v\_0 = \frac{1}{3}(v\_{\rm an} + v\_{\rm bn} + v\_{\rm cn})\tag{1}$$

Directly measuring three-phase voltage and calculating ZSVC according to Formula (1) can be applied to a sinusoidal power supply. The EMU traction inverter generates the ZSVC inherent in the PWM voltage and related to the PWM modulation mode. Although the frequency produced by the inverter is far from the fundamental frequency, if reasonable compensation and filtering are not carried out, frequency aliasing occurs, and the measurement is affected. The ZSVC measurement of the traction motor in Figure 1 is adopted, wherein both *v*n and *v*nR contain the ZVSC produced by the inverter. *v*0 is the difference between them and only contains the ZVSC caused by the stator ITSC fault and asymmetry, so the three balanced resistors can eliminate the influence of the inverter [42].

**Figure 1.** ZSVC measurement circuit.

#### *2.2. ApFFT Time-Shift Phase Difference Correction Algorithm*

The apFFT time-shift phase difference correction mainly includes two parts, i.e., apFFT and time-shift phase difference correction. The apFFT algorithm can effectively suppress spectrum leakage caused by data truncation [43–45]. As shown in Figure 2, the required data points for the *N*-order spectrum analysis are *x*(−*N* + 1), *x*(−*N* + 2), ..., *x*(−1), *x*(1), ..., *x*(*N* − 2), *x*(*N* − 1) with a total of 2*N* − 1 data points.  When both W1 and W2 are rectangular windows, the spectrum analysis approach is referred to as

windowless apFFT. When either W1 or W2 is a rectangular window, the spectrum analysis approach is referred to as single-window apFFT. When neither W1 nor W2 is a rectangular window, the spectrum analysis approach is referred to as double-window apFFT.

**Figure 2.** N-order apFFT calculation method.

The N-order apFFT spectrum analysis mainly includes the preprocessing of 2*N* − 1 point data and the FFT calculation. If the data are processed by windowless apFFT, the following operations can be performed on the data. First, divide 2*N* − 1 data points into *N* segments with length *N* according to Formula (2):

$$\begin{array}{l} \mathbf{x}\_{0} = \left[ \mathbf{x}(0), \mathbf{x}(1), \mathbf{x}(2), \dots, \mathbf{x}(N-1) \right]^{T} \\ \mathbf{x}\_{1} = \left[ \mathbf{x}(-1), \mathbf{x}(0), \mathbf{x}(1), \dots, \mathbf{x}(N-2) \right]^{T} \\ \mathbf{x}\_{2} = \left[ \mathbf{x}(-2), \mathbf{x}(-1), \mathbf{x}(0), \dots, \mathbf{x}(N-3) \right]^{T} \\ \vdots \\ \mathbf{x}\_{N-1} = \left[ \mathbf{x}(-N+1), \mathbf{x}(-N+2), \dots, \mathbf{x}(0) \right]^{T} .\end{array} \tag{2}$$

Then, rotate the *N* segment data of Formula (2), taking the *N*th data point, i.e., *x*(0), as the first data point of the data segment. Formula (2) is transformed into Formula (3):

$$\begin{array}{l} \mathbf{x}\_{0} = \left[ \mathbf{x}(0), \mathbf{x}(1), \mathbf{x}(2), \dots, \mathbf{x}(N-1) \right]^{T} \\ \mathbf{x}\_{1} = \left[ \mathbf{x}(0), \mathbf{x}(1), \dots, \mathbf{x}(N-2), \mathbf{x}(-1) \right]^{T} \\ \mathbf{x}\_{2} = \left[ \mathbf{x}(0), \dots, \mathbf{x}(N-3), \mathbf{x}(-2), \mathbf{x}(-1) \right]^{T} \\ \vdots \\ \mathbf{x}\_{N-1} = \left[ \mathbf{x}(0), \mathbf{x}(-N+1), \dots, \mathbf{x}(-2), \mathbf{x}(-1) \right]^{T} . \end{array} \tag{3}$$

Finally, add the shifted *N* segments of the data separately and normalize them to obtain *x*ap in Formula (4), which comprises the *N* data points obtained after the windowless apFFT preprocessing.

$$\mathbf{x\_{ap}} = \frac{1}{N} [\mathbf{Nx}(0), (N-1)\mathbf{x}(1) + \mathbf{x}(-N+1), \dots, \mathbf{x}(N-1) + (N-1)\mathbf{x}(-1)]^T. \tag{4}$$

Perform N-point FFT on *x*ap; that is, obtain the calculation result *X*ap(*k*) of windowless apFFT, and *k* is the spectrum index.

The second part of the apFFT time-shift phase difference correction algorithm is the time-shift phase difference correction [46]. The single-frequency complex exponential signal with frequency *ω*\*, initial phase *θ*0, and amplitude *A* is *x*(*n*), where *n* is the discrete time point.

$$\mathfrak{x}(n) = A e^{j(w^\*n + \theta\_0)},\tag{5}$$

The data points are divided into two segments of the same length, as shown in Figure 3. The data interval of the first segment is [−*N* + 1, *N* − 1]. Assuming the spectrum serial number is *k*\*, the phase value of apFFT main spectrum line is:

$$
\varphi\_X(k^\*) = \theta\_{0\prime} \tag{6}
$$

**Figure 3.** Data truncation for time-shift phase difference correction.

The second data segment starts after *L* data points of the first data segment. The data range is [−*N* +1+ *L*, *N* − 1 + *L*]. The central data point of this data segment is *x*(−*L*), as shown in Figure 3. If apFFT is performed on the second segment of data, the phase of apFFT main spectral line is *ϕXL*(*k*∗), which is the approximate estimation of the phase of data point *x*(−*L*); that is,

$$
\varphi\_{\rm NL}(k^\*) = \theta\_0 - \omega^\* L,\tag{7}
$$

The estimation of signal frequency can be obtained from Formulas (6) and (7):

$$
\hat{\omega}^\* = \left[ \varphi\_X(k^\*) - \varphi\_{XL}(k^\*) \right] / L = \Delta \varphi / L,\tag{8}
$$

To eliminate the "phase ambiguity" phenomenon [47], the frequency estimation after phase compensation is [48]

$$
\hat{\omega}^\* = \left[ \varphi\_X(k^\*) - \varphi\_{\rm XL}(k^\*) \right] / L + 2k^\* \pi / N,\tag{9}
$$

For the double-window apFFT, the signal amplitude estimation can be obtained:

$$\hat{A} = \frac{|Y(k^\*)|}{\left| F\_{\mathbb{B}}(k^\* \Delta \omega - \hat{\omega}^\*) \right|^2}. \tag{10}$$

In Equation (10), Y(*k*\* ) is the value of the double-window apFFT at point *k*\* ; *F*g(*k*∗Δ*ω* − *ω*ˆ ∗) is obtained from bringing (*k*∗Δ*ω* − *ω*ˆ ∗) into the Fourier transform of the window function. Generally, the window function is a cosine window, and its Fourier transform expression is determined.

#### **3. Fault Diagnosis Method for Stator ITSC Fault of Traction Motor**

#### *3.1. Stator ITSC Fault Extent Index*

In previous studies, only the metallic short circuit of windings is generally considered. The two windings are directly short-circuited without any resistance, and the motor's ITSC fault extent is evaluated based on the number of short-circuit turns. In most cases, the metallic ITSC fault is caused by the expansion of the non-metallic ITSC fault. The non-metallic ITSC fault means that there is some resistance left between short-circuit turns. In this case, the number of short-circuit turns alone is insufficient for evaluating the ITSC fault. The heat caused by the ITSC fault mainly results in damage to the traction motor. If the heat generated by the inductance is ignored, the thermal power of the inter-turn resistance is

$$P\_{\rm f} = \frac{\mathcal{U}\_{\rm f}}{R\_{\rm f}}^2 = \frac{\left(N\_{\rm f} \ast \frac{\mathcal{U}}{N\_{\rm s}}\right)^2}{R\_{\rm f}} = \mathcal{U}^2 \ast \frac{N\_{\rm f}^2}{N\_{\rm s}^{\prime 2} \ast R\_{\rm f}},\tag{11}$$

where *P*f is the thermal power of the inter-turns resistance, *U*f is the short-circuit turns voltage, *R*f is the inter-turn resistance, *U* is the motor phase voltage, and *N*s is the total number of turns of each phase winding. From Formula (11), it can be concluded that the heat generated by the short-circuit current after the ITSC fault is in direct proportion to *N*f 2 *N*s 2∗*R*f .

Define the fault extent index of ITSC fault:

$$
\lambda\_{\rm f} = \sqrt{\frac{N\_{\rm f}^2}{N\_{\rm s}^2 \ast R\_{\rm f}}} = \frac{N\_{\rm f}}{N\_{\rm s}} \ast \frac{1}{\sqrt{R\_{\rm f}}}.\tag{12}
$$

According to Formula (12), the ITSC fault extent index *λ*f is related to the number of short-circuit turns and the inter-turn resistance.

#### *3.2. SVM Model for Diagnosis of ITSC Fault*

The apFFT time-shift phase difference spectrum correction algorithm is used to calculate the fundamental frequency of the ZSVC, the fundamental component amplitudes of the traction motor's ZSVC, and the three-phase current. The SVM-based fault diagnosis model of ITSC fault is established with the five parameters as input. The traction motor ITSC condition is classified as a normal condition, incipient fault condition, and serious fault condition using the proposed index. SVM is a machine learning method based on statistical theory, mainly used to solve classification and regression problems [49–52]. Its core idea is to complete the model training based on the structural risk minimization principle. It has nonlinear solid approximation ability, good generalization performance, and good results in dealing with small samples and nonlinear problems. SVM uses nonlinear mapping *φ*(**x**) to map the original data to the high-dimensional space to deal with nonlinear regression problems of multidimensional data.

The C-SVC model is a relatively standard two-class SVM model. The train set is

$$\mathbf{T} = \{ (\mathbf{x}\_1, \mathbf{y}\_1), \dots, (\mathbf{x}\_l, \mathbf{y}\_l) \} \in (X \times Y)^l,\tag{13}$$

where **x***i* ∈ *X* = **R***n*, **y***i* ∈ *Y* = {1, −1}(*i* = 1, 2, ··· , *l*), and **x***i* is the features vector.

Select kernel function *K*(*x*, *x* ) and appropriate parameter C. The standard kernel functions *K*(*x*, *x* ) mainly include linear, polynomial, and radial basis kernel functions. The Lagrange dual problem of the original problem is

$$\min\_{\boldsymbol{\alpha}} \frac{1}{2} \sum\_{i=1}^{l} \sum\_{j=1}^{l} y\_{i} y\_{j} \boldsymbol{a}\_{i} \boldsymbol{a}\_{j} \boldsymbol{\mathcal{K}} (\boldsymbol{x}\_{i}, \boldsymbol{x}\_{j}) - \sum\_{j=1}^{l} \boldsymbol{a}\_{j},\tag{14}$$

$$\text{s.t.} \sum\_{i=1}^{l} y\_{i} \boldsymbol{a}\_{i} = \boldsymbol{0}, \; \boldsymbol{0} \le \boldsymbol{a}\_{i} \le \boldsymbol{\mathcal{C}}, i = 1, \cdots, l$$

Obtain the optimal solution: α∗ = *α*∗ 1, ··· , *α*∗ *l* T .

Select a positive component of 0 < *α*∗ *j* < *C* from α∗, and calculate the threshold accordingly:

$$b^\* = y\_j - \sum\_{i=1}^l y\_i \alpha\_i^\* K(\boldsymbol{x}\_i - \boldsymbol{x}\_j)\_\prime \tag{15}$$

The constructed decision function is

$$f(\mathbf{x}) = \text{sgn}\left(\sum\_{i=1}^{l} a\_i^\* y\_i \mathcal{K}(\mathbf{x}, \mathbf{x}\_i) + b^\*\right),\tag{16}$$

If the Gaussian radial basis function is used as the kernel function, g is the parameter of the Gaussian radial basis function:

$$K(\mathbf{x}\_i, \mathbf{x}) = \exp\left(-\frac{||\mathbf{x}\_i - \mathbf{x}\_j||^2}{2\sigma}\right) = \exp\left(-g\left||\mathbf{x}\_i - \mathbf{x}\_j||^2\right). \tag{17}$$

In the SVM classification model, the selection of the model penalty parameter C and Gaussian kernel function parameter g is directly related to the model performance. The K-CV (K-fold cross-validation) is a standard cross-validation algorithm that divides datasets into K sub-datasets evenly in model training. Each sub-dataset is used as the validation set in turn, and the remaining K − 1 sub-datasets are used as the training set to train K models. The average mean square error (MSE) of K models on the validation set is used as the performance index. The mean square error is

$$\delta\_{\rm MSE} = \frac{1}{n} \sum\_{i=1}^{n} \left( y\_i - \overline{y}\_i \right)^2. \tag{18}$$

In Formula (18), *n* is the number of samples, *yi* is the predicted value, and *yi* is the target value.

Grid search is used to select several discrete points on each dimension of the parameter space according to certain rules. The discrete points of different dimensions intersect in the parameter space to obtain the discrete solution. Calculate each discrete solution to obtain the optimal solution. Figure 4 is the flow chart of the hyper-parameter optimization using K-CV and the grid search method. Take the grid point as C = 2a,g=2b, and initialize the range of a and b, which are positive and negative integers. Parameters a and b are selected from the minimum to the maximum using 1 step of their range. K-CV is used to calculate the average MSE of the K models on the K validation sets. After calculating all the combinations of C and g at all grid intersections, C and g at the minimum average MSE are the optimal solutions.

**Figure 4.** Flow chart of the hyper-parameters optimization.

#### *3.3. ITSC Fault Diagnosis Procedure Based on SVM Model*

As shown in Figure 5, the EMU traction motor ITSC fault diagnosis based on SVM includes two stages: model training and online diagnosis. In the model training stage, the ZSVC and three-phase current are first measured with the circuit proposed in the article. Second, the apFFT time-shift phase difference correction algorithm is used to calculate the ZSVC fundamental frequency, the fundamental amplitudes of ZSVC, and the threephase current. Third, the ZSVC fundamental frequency, the amplitude of ZSVC, and the three-phase current are used as features. Based on the ITSC fault index λf, the samples are divided into three categories: normal, incipient, and serious faults. Fourth, the K-CV method divides all the samples into training and validation samples. K-CV and the grid search method are used to optimize the hyper-parameters. Last, the optimal ITSC fault diagnosis model is saved. In the online diagnosis stage, the ITSC fault features are acquired similarly to the training stage. The optimal ITSC fault diagnosis model is loaded, and the fault features are input into the SVM model to predict the ITSC category.

**Figure 5.** Procedure of the ITSC fault diagnosis based on SVM model.

#### **4. EMU Electric Traction Simulation Experimental Platform**

*4.1. Overall Design of the Experimental Platform*

The experimental data are acquired from the mutual-feed electric traction simulation experimental platform shown in Figure 6. The platform mainly includes the tested system and the load system. The tested system mainly includes an S120 variable frequency speed control system and the tested motor. The S120 controls the tested motor to operate according to the experimental conditions. The S120 system mainly includes the CU320-2PN control unit, ALM rectifier, and MM inverter modules. The load system mainly includes the

braking motor and the H1000 converter. The PCI-6229 NI-DAQ gives the H1000 converter work instructions. The DC power supply of the tested system is obtained from the DC link of the load system. When the tested motor works in the motor state, the braking motor works in the generator state. The load system feeds electric energy back to the DC link to realize DC energy mutual feedback.

**Figure 6.** Energy mutual-feed electric traction simulation experimental platform.

Figure 7 shows the main parts of the experimental platform. The tested motor is a three-phase AC asynchronous squirrel-cage motor with three-phase winding taps pulled out, whose parameters are shown in Table 1. The braking motor is a normal motor with the same type and power.

**Figure 7.** Main parts of the EMU electric traction simulation experimental platform.

**Table 1.** Rated parameters of the tested motor.

#### *4.2. Setting ITSC Faults on Tested Motor*

Figure 8 shows that the winding taps are pulled out at different stator winding turns during manufacturing to simulate the ITSC fault. The taps can be connected externally to simulate the short-circuit fault between different turns. The power resistor simulates the inter-turn resistor between non-metallic short-circuit turns. The vacuum breaker conveniently controls the short circuit of different turns loop.

**Figure 8.** Stator winding taps pulled out of the tested motor.

*4.3. Signal Measurement of the Experimental Platform*

The signal measurement is shown in Figure 9. The DL850E ScopeCorder produced by Yokogawa corporation in Japan is used for signal measurement whose LPF (low pass filter) is set to 400 Hz, and the sampling frequency is 2000 Hz. The A621 passive current probe is used to collect the inter-turn current, which cannot be measured in the actual application. If the inter-turn current is too large, it generates heat quickly to burn the motor. E3N active current probe is used to measure the three-phase current of the tested motor. The DP-50 voltage probe is used to measure the ZSVC using the measurement circuit shown in Figure 2. The ZSVC measurement balanced resistors are three 15 kΩ (1 kW) power resistors.

**Figure 9.** Signal measurements of the tested motor.

#### **5. Analysis of ITSC Fault Diagnosis Model Based on Experimental Samples**

During the experiment, the S120 converter system controlled the tested motor to operate in the torque control mode outputting a fixed electromagnetic torque. The H1000 converter controlled the braking motor according to the speed control mode running at a fixed speed. This experimental operation mode could simulate the steady operation conditions of EMU traction or electric braking at different speeds and torques.

#### *5.1. Analysis of Motor Signals with ITSC Fault*

The tested fault motor ran at 900 rpm rotating speed with 10 Nm electromagnetic torque, 12 short-circuit turns ITSC fault in the a-phase stator winding, and a 1 Ω inter-turn resistor, as shown in Figure 10.

**Figure 10.** The stator ITSC fault set on the a-phase.

The insulation fault occurred at around 20 s. It can be seen from Figure 11a that when the stator winding ITSC fault occurs, a sinusoidal inter-turn current with the same fundamental frequency as the power supply is generated between the short-circuit turns. Figure 11b shows the three-phase current before and after the ITSC fault. Although the amplitude of the short-circuit current reached about 10 A, it had little impact on the threephase current. Figure 11c shows the S120 system outputting three-phase voltage filtered by the 400 Hz LPF filter, and the outputting waveform conforms to the saddle waveform of SVPWM. Figure 11d shows the ZSVC before and after the ITSC fault, and the ZSVC will be studied and analyzed later.

**Figure 11.** Signals of the tested system before and after ITSC fault: (**a**) inter-turn current of the tested motor; (**b**) three-phase current of the tested motor; (**c**) three-phase voltage of S120 inverter module; (**d**) ZSVC of the tested motor.

#### *5.2. Analysis of ITSC Fault Features*

The tested motor setting speed was 900 rpm, and the electromagnetic torque was set to 10 Nm. There was an ITSC fault in the a-phase winding. The frequency of the ZSVC fundamental component, the fundamental amplitudes of ZSVC, and the three-phase current were calculated using the apFFT time-shift spectrum correction algorithm with double Hanning windows. Based on Formula (12), 20 different indexes λf were calculated according to 5 different numbers of short-circuit turns and 4 different inter-turn resistances, as shown in Table 2.

**Table 2.** The ITSC fault set and the extent index λf.

It can be seen from Figure 12a that the ZSVC fundamental amplitude of the tested motor increased with the fault extent index λf. The fundamental amplitude of the ZSVC was about 0.2 V under normal conditions, mainly caused by the asymmetry of the three-phase winding. It can be seen from Figure 12b that the fundamental amplitude of the a-phase current increased with the ITSC fault extent. The b-phase and c-phase currents changed little. Similarly, because of the unbalance of the three-phase winding, the three-phase current was unbalanced under normal conditions.

**Figure 12.** Influence of ITSC fault on the tested motor signal fundamental amplitude: (**a**) influence of ITSC fault on ZSVC fundamental amplitude; (**b**) influence of ITSC fault on three-phase current fundamental amplitude.

The electromagnetic torque was set to 10 Nm, and the ITSC fault extent index λf was 0.07317. It can be seen from Figure 13a that in the process speed regulation, the fundamental frequency changed with the experimental system setting speed. According to the control characteristics of variable frequency speed regulation, the three-phase voltage increased linearly with the increase in speed. The ZSVC also increased with the tested motor's fundamental frequency under the same λf. Figure 13b shows that the a-phase current did not change much, but the b-phase and c-phase currents decreased significantly with the increase of the fundamental frequency. The asymmetry of the three-phase current became more and more prominent.

**Figure 13.** Influence of frequency on the tested motor signal fundamental amplitude: (**a**) influence of frequency on ZSVC fundamental amplitude; (**b**) influence of frequency on three-phase current fundamental amplitude.

According to the analysis above, the ZSVC fundamental component amplitude and the three-phase current asymmetry increases with the ITSC fault extent under fixed electromagnetic torque and speed. The three-phase current amplitude can reflect the electromagnetic torque value, and the speed is approximately linear with the fundamental frequency. Therefore, the ZSVC fundamental frequency, the fundamental amplitudes of ZSVC, and the three-phase current are selected as the features to establish the ITSC fault diagnosis model.

#### *5.3. Analysis of SVM ITSC Fault Diagnosis Model Performance*

The tested motor's data acquisition conditions are shown in Table 3. The tested motor with each fault extent index operated under four different speeds and electromagnetic torques. There were 16 working conditions in which the speeds or electromagnetic torques were different, and 7 normal samples needed to be acquired at each working condition to first obtain a total of 112 normal samples. There were 20 different ITSC fault extent *λ*f samples under each speed and electromagnetic torque, as shown in Table 2. The classification of fault severity categories is determined by the application situation defined by users. In the experiment, the samples with 0.03018 ≤ λf < 0.06098 were defined as incipient ITSC fault samples because the thermal power caused by inter-turn short-circuit fault was small. Samples with 0.06098 ≤ *λ*f were considered serious ITSC fault samples because the thermal power was large. Thus the ITSC fault samples were divided into 112 incipient and 112 serious ITSC fault samples. The SVM-based ITSC fault diagnosis model was established by selecting 92 samples from each category as train samples and 20 samples from each category as the test samples. The grid search range was a = [−5, 5], b = [−5, 5]. The parameter K was 3 in the K-CV method.

**Table 3.** The working condition and the ITSC fault setting.

Figure 14 shows the prediction results of the SVM-based ITSC fault diagnosis model on the experimental samples. Figure 14a shows that there is no misclassification on the training dataset. Figure 14a shows that there is a sample misclassified among the normal and incipient samples and a sample misclassified among incipient and serious samples. There is no misclassification between the normal and the serious fault samples. The model's prediction accuracy is 100% on the training dataset and 93.33% on the test dataset, which indicates that the model can detect and evaluate the ITSC fault accurately.

**Figure 14.** Confusion matrix of ITSC fault diagnosis SVM model: (**a**) confusion matrix of SVM model on the training dataset; (**b**) confusion matrix of SVM model on the test dataset.

#### **6. Conclusions**

The ITSC fault diagnosis of the asynchronous traction motor significantly ensures the EMU's safe operation and saves maintenance costs. The non-metallic ITSC fault extent evaluating index λf was proposed based on the short-circuit thermal power. The index λf is related to the number of short-circuit turns and inter-turn resistance. The apFFT time-shift phase difference spectrum correction with double Hanning windows was used to calculate five parameters used as fault features to train SVM model to diagnose the ITSC fault, and the SVM model hyper-parameters C and g were optimized using K-CV and the grid search method. The method proposed in the article can detect and evaluate the ITSC fault of traction motors in a speed control system under vector control or direct torque control conditions in which the fundamental frequency of supply voltage is unknown. EMU traction motors work at different speeds and torque points during operation. The prediction results of different steady-state operating points can be integrated to improve the accuracy of the fault diagnosis model. The method can be used when the traction motor is in a steady state, and it cannot be used if the traction motor accelerates or decelerates.

**Author Contributions:** Methodology, J.M.; software, G.Z.; validation, J.H.; formal analysis, J.M. and J.H.; investigation, Z.Z.; resources, J.F.; data curation, J.M.; writing—original draft preparation, J.M. and J.H.; writing—review and editing, X.L.; visualization, X.L.; supervision, J.F. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the 2021 Scientific Research Fund Project of Liaoning Provincial Department of Education (LJKZ1297).

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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## *Review* **Physical Variable Measurement Techniques for Fault Detection in Electric Motors**

**Sarahi Aguayo-Tapia, Gerardo Avalos-Almazan, Jose de Jesus Rangel-Magdaleno \* and Juan Manuel Ramirez-Cortes**

> Digital Systems Group, Electronics Department, Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro #1, Tonantzintla, Puebla 72840, Mexico<EMAIL_ADDRESS>(S.A.-T<EMAIL_ADDRESS>(G.A.-A<EMAIL_ADDRESS>(J.M.R.-C.) **\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** Induction motors are widely used worldwide for domestic and industrial applications. Fault detection and classification techniques based on signal analysis have increased in popularity due to the growing use of induction motors in new technologies such as electric vehicles, automatic control, maintenance systems, and the inclusion of renewable energy sources in electrical systems, among others. Hence, monitoring, fault detection, and classification are topics of interest for researchers, given that the presence of a fault can lead to catastrophic consequences concerning technical and financial aspects. To detect a fault in an induction motor, several techniques based on different physical variables, such as vibrations, current signals, stray flux, and thermographic images, have been studied. This paper reviews recent investigations into physical variables, instruments, and techniques used in the analysis of faults in induction motors, aiming to provide an overview on the pros and cons of using a certain type of physical variable for fault detection. A discussion about the detection accuracy and complexity of the signals analysis is presented, comparing the results reported in recent years. This work finds that current and vibration are the most popular signals employed to detect faults in induction motors. However, stray flux signal analysis is presented as a promising alternative to detect faults under certain operating conditions where other methods, such as current analysis, may fail.

**Keywords:** fault detection; fault classification; induction motors; measurement techniques; physical variables; signal analysis

#### **1. Introduction**

Induction motors (IMs) are the principal source of power in the manufacturing industry due to their sturdiness, energetic efficiency, and repair and maintenance costs.

IMs are prone to present faults associated with thermal, mechanical, and magnetic stresses or to expected causes, such as natural wear of bearings [1]. Bearing damage is the type of fault that appears more frequently. However, other faults may appear due to unbalanced loads, unbalanced power supply, and high operational frequencies, among others. Typically, the faults with a higher frequency of appearance are: bearing faults, stator winding faults, and rotor broken bar faults [2,3].

Researchers have documented advances in IMs' condition monitoring and fault detection in this context. Atta et al. [4] summarized recent techniques for broken bar faults under starting and steady-state conditions using current, voltage, flux, vibration, and acoustic signatures. In that work, the authors subgroup the methods based on resistance estimation, parameter estimation, digital estimation, time-domain, frequency domain, time–frequency domain, and data-driven approaches. The main drawback of this work is that it is highly specialized, given that it focused only on one type of fault, in this case, broken bar fault detection. However, it provides a good panorama of the evolution of

**Citation:** Aguayo-Tapia, S.; Avalos-Almazan, G.; Rangel-Magdaleno, J.d.J.; Ramirez-Cortes, J.M. Physical Variable Measurement Techniques for Fault Detection in Electric Motors. *Energies* **2023**, *16*, 4780. https:// doi.org/10.3390/en16124780

Academic Editors: Daniel Morinigo-Sotelo, Rene Romero-Troncoso and Joan Pons-Llinares

Received: 4 May 2023 Revised: 4 June 2023 Accepted: 15 June 2023 Published: 18 June 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

broken bar fault detection techniques in the last 8 years. The authors pointed out that some issues still require attention, such as methods for multiple-fault detection, model-based methods, transfer learning in driven-based methods, and assessing the performance of time–frequency methods under short-start duration, among others. Gundewar et al. [5] reviewed recent techniques used for fault diagnosis in IM according to the type of fault: bearing fault (BF), rotor bar faults, air gap eccentricity, and stator faults. Moreover, they highlighted the use of artificial intelligence (AI) techniques for condition monitoring, such as support vector machines (SVMs), fuzzy logic, adaptive neuro-fuzzy inference system (ANFIS), and genetic algorithm (GA). They provided an extensive overview of different types of IM faults. Nevertheless, the techniques for fault detection are limited to AI-based, which have been proven to have high detection accuracies but at the cost of high computational resources required for its implementation. A review on IMs fault detection based on current analysis was presented by Yakhni et al. [6], focusing on applications with variable speeds. The authors summarized the methods based on current analysis for detecting particular faults: broken rotor bar, bearing, eccentricity, stator fault, inter-turn short circuit, and unbalanced supply. This paper contributes to reviews of current-based analysis. However, it only focuses on two approaches: the adaptive observer and the adaptive notch filtering method.

This paper presents a revision of the recent works and tendencies in the types of physical variables employed for fault detection. Although previous works have discussed advances in fault detection and condition monitoring, these are mainly centered on the type of fault or the methods themselves, not so in detail regarding the type of physical variable.

#### **2. Common Types of Faults in Induction Motors**

Different types of physical variables are measured and analyzed to detect the presence of a fault in IM. Induction motors are prone to present faults associated with electrical, mechanical, or environmental aspects. Electrical faults can be induced, among others, by supply current unbalance and overload, and faults such as short circuits are produced. Mechanical faults, however, are related to broken bars and bearing damage. Certain environmental conditions, such as humidity and very high or low temperatures, may affect the IM and progress into electrical or mechanical faults. Some of the faults that appear in IMs more commonly are presented next.

#### Broken Rotor Bars

Damage in the rotor bars is difficult to detect since the induced spectral components lie near the supply frequency. Moreover, as the severity of the fault increases, the spectral components are greater in amplitude. As the load of the motor increases, the fault components are further from the fundamental frequency. Hence, incipient faults, commonly less than half broken bars, are more difficult to detect; the load condition also affects the fault detection accuracy.

The fault is created synthetically by drilling the bar to study broken rotor bars, as illustrated in Figure 1. Most works that study the presence of broken bars in IMs analyze 1 and 2 BRB; few works are able to detect less than 1 BRB, given that it usually requires complex algorithms for signal processing.

Broken rotor bars are commonly detected by means of motor current signature analysis, where the current spectrum is processed and analyzed, searching for changes in the amplitude of certain frequency spikes. These components usually appear near the fundamental frequency, as the following equations indicate this:

$$f\_{bb} = [\frac{h}{p}(1-s) \pm s]f\_s \tag{1}$$

where *fbb* are the spurious frequencies, *p* represents the number of pairs of poles, *s* is the slip, *fs* represents the frequency of the grid, and *h* is an index that represents the number of harmonics.

In a similar way, the localization of spurious frequencies can be performed by using vibration analysis, following the next equation:

$$f\_{bk\pm} = f\_s[1 \pm 2ks] \tag{2}$$

where *fbk*± represents the spurious frequency, *k* represents an integer index, *fs* is the frequency of the grid, and *s* represents the slip.

**Figure 1.** Rotor bar drilled to emulate a one broken bar fault.

Bearing Damage

It is well known that bearings in an IM are fundamental elements since this allows the rotor to rotate on its own axis. Due to its mechanical nature, bearings are under mechanical and electrical stresses. A common IM bearing is conformed by static and mobile elements, as shown in Figure 2.

The seals maintain the lubricant inside the bearing and keep the contaminants out; the mechanic load is transferred from a foothold to a rolling point, i.e., from the inner raceway to the outer raceway through the rolling elements (balls or rollers), and the cage maintains the rolling elements in its position.

**Figure 2.** Bearing parts.

Bearing faults are mostly identified using vibration signals [7,8]. Spurious frequencies based on vibration analysis can be localized according to the next equations [9]:

$$f\_o = \frac{N\_b}{2} f\_r [1 - \frac{Db}{Dc} \cos(\beta)] \tag{3}$$

$$f\_i = \frac{N\_b}{2} f\_r [1 + \frac{Db}{Dc} \cos(\beta)] \tag{4}$$

$$f\_b = \frac{D\_b}{D\_c} f\_r [1 - \frac{Db^2}{Dc^2} \cos(\beta)^2] \tag{5}$$

where *fo*, *fi*, and *fb* represent the spurious frequencies for outer race damage, inner race damage, and bearing ball damage, respectively; *Nb* represents the number of balls; *β* represents the angle between the ball and the defect; *Db* is the diameter of the ball; and *Dc* is the pass diameter.

In this way, previous works have studied bearing faults through vibration analysis, expecting the appearance of the fault's frequencies, as stated by the aforementioned equations.

#### Short Circuit

The most recurrent faults related to a shortcut are the stator winding and rotor shortcuts. A short circuit is one of the most difficult faults to detect; protection systems commonly detect the shortcut when damage is critical. A grounded shortcut results in severe damage to the machine's integrity.

There are several approaches used for shortcut detection. The most used are as follows:

If a stator fault exists, the current through the shorted winding will cause variations in the normal magnetic field and variations in the axial flux according to the next equation [10]:

$$f\_{ss} = f\_s[(k \pm n\frac{1-s}{\rho})] \tag{6}$$

where *fs* is the grid frequency, *n* is the order of the shorted coil space harmonic, *k* is the harmonic order, *s* represents the slip of the machine, and *ρ* is the number of pole pairs.

#### *2.1. Effects of Vibrations in Induction Machines*

Faults in induction machines may cause negative effects in the normal operation of the motor regardless of the cause; some of the principal vibration sources are voltage/current imbalances, rotor bar defects, bearing defects, voltage distortion of the power grid, and speed driver issues, among others. There are two main sources of vibrations: mechanical and electromagnetic origin.

Vibrations of mechanical origin are mainly caused by rotor imbalance, shaft bow, and misalignment or wear in mechanical elements.

Electromagnetic vibrations provoke for radial electromagnetic forces and tangential electromagnetic forces. Under normal operation conditions, these forces do not represent significant vibrations. Excessive electromagnetic motor vibrations are often a result of a resonance condition on the structure of the motor as a unit or even on the motor components [11]. Vibrations may cause or accentuate problems, such as misalignment and eccentricity, among others. These problems are discussed next.

#### *2.2. Misalignment*

Misalignment can be caused by the deviation of the coupling axis between the load and motor shafts. Excessive vibrations will provoke asymmetries in the motor frame, shaft, and bearings in the IM. If the misalignment fault is not detected early, the consequences could be bearing damages, eccentricity, and gearbox failures.

The two types of misalignment issues in IMs are parallel and angular faults. A parallel fault occurs when there is a height difference between the shaft centers of the load and the IM, and an angular fault occurs when an inclination exists between the load and the IM. The combination of parallel and angular misalignment is called mixed misalignment. The coupling differences between the shafts emerge in the frequency spectrum of current as follows [12]:

$$f\_{mc} = [1 \pm k(1 - s)/p]f\_s \tag{7}$$

where *fmc* represents the spurious frequency of the misalignment signal frequency, *k* is an integer operator, *p* represents the number of pole pairs, *fs* is the supply frequency, and *s* is the slip of the motor. Similarly, the spurious frequency can be obtained for the vibration spectrum as follows,

$$f\_{\rm mv} = kf\_r \tag{8}$$

where *fr* is the rotational frequency.

#### *2.3. Eccentricity Air Gap*

Eccentricity can be classified as dynamic and static. Dynamic eccentricity occurs when the center of the rotor is not at the center of rotation, and the position of the minimum radial air gap rotates with the rotor. Static eccentricity occurs when the position of the minimum radial air gap is fixed. Both static and dynamic eccentricity produce a polar pair, *p* ± 1, in the spatial distribution of the magnetic field in the air gap, where *p* represents the number of the fundamental polar pair [13,14].

An eccentricity fault provokes spurious components in the current spectrum according to the following equation:

$$f\_h = (2(kR \pm n\_d)(\frac{1-s}{p}) \pm v)f\tag{9}$$

where *nd* represents the eccentricity order equal to 0 for statistic eccentricity and integer coefficient 1, 2, 3 ... for dynamic eccentricity; *f* is the frequency of the power supply; *s* represents the slip; *p* is the number of poles; *R* represents the number of rotor bars; *k* is a positive integer coefficient; and, finally, *v* represents the harmonic order of the stator supply voltage [15].

In the next section, fault detection techniques are discussed.

#### **3. Analysis of Faults Based on Physical Variables**

According to the features of the fault detection techniques, these can be classified into model-based, signal-based, and data-based techniques, as illustrated in Figure 3. Depending on the type of physical variable, some of these methods are more adequate than others for fault detection.

**Figure 3.** A classification of fault detection methods.

For instance, a method based on models is proposed using flux signals, where a finite element method (FEM) simulation is implemented, as illustrated in Figure 4. These methods usually imply converting the flux signals into the DQ frame.

**Figure 4.** FEM simulation for a fault detection method based on flux signals: (**a**) mesh model, (**b**) stator circuit model. Source: [16].

On the other hand, an example of a technique based on signals may comprehend selective filtering of current or vibration signals and statistical analysis, such as the works in [17,18]. The filtering of the signal that is illustrated in Figure 5 depicts a narrow bandwidth Taylor–Fourier filter over a current signal, and Figure 6 illustrates the decision making according to the three standard deviation criteria for fault detection based on statistical distributions of the amplitude of the filtered signals.

**Figure 5.** Filtering of the current signal. Source: [18].

**Figure 6.** Fault detection criteria based on statistical distributions.

An example of a method based on data is presented in Figure 7, where an architecture based on a convolutional neural network is applied to detect electrical faults by using thermal images. Thermal images are commonly processed, as presented in [19], with AI methods.

**Figure 7.** Scheme of a fault detection method based on data. Source: [19].

Next, details regarding detecting faults based on certain types of signals are given.

#### **4. Methodologies for IMs Diagnostic**

Fault detection and classification methodologies depend mainly on the implemented algorithm. The elements to traduce the physical variables to processable information are known as sensors. Sensors are indispensable for measuring physical variables and looking for valuable information for detection and classification. The most used sensors are current and voltage sensors, accelerometers, encoders, thermographic cameras, acoustic sensors, and antennae, among others [20].

Inherent to the sensor's use, data acquisition systems must store the physical variables to be processed. Those systems can be digital or analog; commonly, aiming to avoid complications, data acquisition boards are used. Other possibilities include embedded systems such as microcontrollers or field programmable gate arrays (FPGAs). Commonly, the sampling frequencies can be fixed from a range going from 1 (Khz) to 100 (Mhz), and the data acquisition systems can include filters to avoid aliasing or noise.

A variable analysis is commonly performed in the frequency domain. One of the most used techniques for signal processing is Fourier transform, and recently, techniques based on AI for characterization and classification have been developed. Regardless of the physical variable that is analyzed, machine learning methods have stood out as one of the best options for fault classification and pattern recognition, at the cost of being the most complex algorithms and requiring high computational resources for its implementation. Some of these methods are mentioned next:

system; these kinds of techniques are based on the analysis of the thermal behavior of the faulty region in an induction machine [25].

In the next sections, physical variables and their analysis techniques are listed.

#### *4.1. Current*

Motor current signal analysis (MCSA) refers to the process used to obtain information about the dynamic of the electric machine by signal processing from the stator currents. The current signals in the time domain are usually obtained through current sensors with resistive shunts at their outputs [26]. Frequently, researchers obtain the current samples from different speeds, frequencies, and load conditions to acquire behavioral information on the dynamics under different conditions to apply the detection and classification techniques in practical scenarios, such as industrial applications.

Motor faults affect the spectrum of the stator current signals; several techniques for fault detection in induction motors based on MCSA have been proposed for detecting both electrical and mechanical issues, and commonly, these techniques are based on algorithms for spectrum analysis to find fault signatures, aiming to detect and classify the location and severity of the fault [27–37].

Current-based analysis for fault detection usually involves using a Hall-effect sensor concerning the signal acquisition; these are commonly included in the instrumentation installed in the IMs for control and protection purposes. Therefore, no additional expenses are required, nor is invasive sensing needed. Current signals are studied for fault detection, mostly in the frequency domain, to identify components introduced by the fault, such as broken rotor bars (BRBs) and inter-turn short circuits (ITSCs).

In 2016, Romero et al. [38] proposed a method to detect 1 BRB and eccentricity produced by a motor-load misalignment relying on complete ensemble empirical mode decomposition (CEEMD) and multiple signal classification (MUSIC). In that work, a time– frequency analysis is conducted to identify the faults under the start-up and steady-state regimes of the motor. The authors presented an experimental setup where the current signals were acquired with a Honeywell CSNE151 Hall-effect current sensor. Then, the proposed methodology was compared with others such as STFT, Wavelet and STFT, EMD and STFT, MUSIC, and Wigner–Ville. The results highlighted that CEEMD and MUSIC exhibited better detectability than the other methods under both start-up and steady-state conditions.

In 2021, Yang et al. [37] explored the combination of modified ensemble empirical mode decomposition (MEEMD) energy entropy and ANN to analyze stator current signals. In that work, current signals were decomposed with MEEMD, and a cross-correlation criterion selected the components with more information. Then, the energy entropy of these signals was calculated and stored in a vector. Finally, the vectors fed the ANN to identify BRB and air-gap eccentricity. The work reported a detection accuracy of 99% and an improvement respecting EEMD with ANN and MEEMD with SVM. The authors used simulated data, which can be considered a drawback given that data collected from an experiment usually includes some of the effects that are more likely to be present in the industry.

Various processing methods combined with AI classifiers have been reported in the last few years for fault detection of different types. For instance, in [39], faulty bearings, different levels of short circuits in the stator coils, and different levels of BRB are detected using a multi-agent system based on AI. In other work, for detecting winding insulation burn, bearing damage, and BRB, the authors in [40] proposed using spiking neural networks to analyze the current signals of the IM.

Hence, it is noticeable that the current analysis is very popular for condition monitoring and fault detection in IMs. Table 1 summarizes different works based on current analysis. Each work is divided by year, the type of sensor employed for the signal acquisition, the type of fault that is detected, and the method for the analysis.

**Table 1.** Works based on current analysis.

In Figure 8, aspects related to the analysis of current signals are summarized: the preferred instrument for acquisition, method for analysis, and pros and cons of using this physical variable.

**Figure 8.** Highlights of the current-based analysis.

#### *4.2. Vibrations*

IMs inevitably produce vibrations during operation because of their rotatory nature. Vibrations may cause imbalance and misalignment in the electromechanical system, and those issues are accentuated when a defect exists in the motor. Vibrations directly affect the air-gap eccentricity, which influences the uniformity of the distance between the rotor and the stator, indirectly affecting the stator currents.

Fault signature depends mainly on the geometry of the damaged element. The principal disadvantage of vibration analysis on an IM is system isolation since vibrations of the surrounding elements introduce noise to the measurements. Devices such as accelerometers are commonly used to sense the induction machine's three-axis movements with respect to time [54].

Vibration analysis has been widely studied for fault detection, mainly because most of the faults that can appear in an IM will have an effect on the vibration measurements [55,56]. In 2016, Sun et al. [57] employed a deep neural network (DNN) to classify BRB, bowed rotor, inner race bearing fault, ITSC, and unbalance rotor. This approach utilized a sparse autoencoder based on the measured vibration data for feature learning. Moreover, the authors compared the DNN performance with a neural network, SVM, and linear regression; for the five types of faults, DNN presented the best accuracy results.

In 2019, Lee et al. [58] measured vibration data from an IM with rotor fault and bearing fault and used a CNN for fault classification. Vibration data were measured by means of a vibration sensor and NI-9231. Classification accuracies of 98–100% were reported in this work, highlighting that the vibrations signals did not require a frequency domain transformation, as is commonly required for a current-based analysis.

Other recent works have frequently proposed methods, including neural networks such as CNN and deep convolutional neural networks (DCNNs), for bearing fault classification [59–61]. Some of the pros and cons of vibration-based analysis are summarized in Figure 9.

Recent works on fault detection based on vibration analysis are listed in Table 2.

**Table 2.** Works based on vibrations analysis.

**Figure 9.** Highlights of the vibration-based analysis. Vibration signal taken from [65].

#### *4.3. Temperature*

Thermal analysis is a process based on a nonintrusive and noncontact technique for monitoring systems. In recent years, this kind of analysis has grown in popularity due to the large amount of information provided by thermal images. For instance, when a bearing fault occurs, the friction operation coefficient upon operation increases, which leads to an increase in the temperature of the IM [69].

This kind of analysis is commonly implemented based on complicated computational processes due to image processing. Fault detection based on thermal images requires the use of special cameras; this type of instrument is more unusual than, for example, a current clamp.

Despite the complications of thermal analysis, some works have been committed to proposing new methods for fault detection, such as the one presented by Mahami et al. [70] in 2021, where an infrared thermography technique was applied to detect eight different short circuit faults in the stator winding; a bag-of-visual-words model was used for feature extraction, and the fault patterns were identified using an extremely randomized tree. In that work, the classifier is compared with KNN, SVM, least-squares SVM, random forest, deep-rule-based, and self-organizing fuzzy logic classifier approaches, and the results prove that the extremely randomized tree shows the best accuracy. Thermal analysis based on AI methods is an effective option for fault detection in IMs. Other works also rely on the use of advanced techniques for pattern classification.

In Figure 10, highlights of thermal analysis are given, and in Table 3, recent works are listed. Considerably fewer works are committed to thermal analysis than current or vibration analysis.

#### *4.4. Flux*

Recent methodologies for monitoring systems based on magnetic flux have been gaining attention mainly because some manufacturers include magnetic sensors embedded in their electric motors.

The magnetic-flux-based methodologies can be grouped into stray flux analysis, where the magnetic flux outside the machine is studied, and the air-gap flux is analyzed. In general, flux analysis is noninvasive, sensors are low cost, and their installation is simple.

**Figure 10.** Highlights of the thermal-based analysis. Thermal image taken from [70].

**Table 3.** Works based on temperature analysis.

In 2016, Mirzaeva et al. [74] proposed an online fault diagnostic system based on internal main air-gap flux density measurements using Hall-effect sensors; stator turnto-turn shorts, rotor bar damage, and dynamic and static eccentricity were detected with this system. Using this method, faults were detected, and their location and severity were obtained. Moreover, the flux density measurements provided time and space dimensions, offering more possibilities for condition monitoring.

In 2019, spectrum analysis of the radial stray flux was proposed by Park et al. [75] to detect one BRB and two BRBs. This paper demonstrates the reliability of this proposal over the current analysis under the influence of rotor axial air ducts, a condition that produces magnetic asymmetries and may lead current-based detection to fail. Another advantage of this method is the low-cost flux coil and the sensitivity and reliability of this flux-based fault detection.

Minervini et al. [76] used current and flux signals to classify bearing faults by using a pre-trained DCNN. In that work, two severity levels of generalized roughness and localized defects in the outer ring of the bearing were analyzed. It is important to highlight that, in this case, the flux signal provided 100% accuracy, contrasting with an accuracy of 80% when using current signals. The authors suggested that this difference may be attributed to the fact that the flux sensor could be installed very close to the faulted bearing, given the size of the motor.

Overall, the flux analysis provides good results for IM fault detection, whereas another kind of methodology based on another physical phenomenon exhibits false positives. The drawback of flux-based methods is the restriction for remote monitoring, unlike the current signals that are usually the base of condition monitoring of IMs.

In Figure 11, some highlights of the flux analysis are given, and recent works based on this physical variable are presented in Table 4.

**Table 4.** Works based on flux analysis.

**Figure 11.** Highlights of the flux-based analysis. Flux signal taken from [79].

#### *4.5. Combined Analysis*

Aiming to obtain better results, researchers have explored two or even three modal signal analyses to take advantage of the properties of the different variables from the IM.

It is noticeable from Table 5 that most works combine current and vibration signals, taking advantage of the wide knowledge built around analyzing these variables. For instance, in 2017, Cruz et al. [80] processed current and vibration signals in the time– frequency domain by using short-time Fourier transform and wavelet multiresolution analysis. With this method, authors detected failures in the motor helix and unbalanced loads in the motor shaft and classified them using fuzzy logic.

AI methods were employed by Zawad et al. in 2019 [81] for detecting unbalanced shaft rotation; bearing fault; unbalanced voltage; one, two, and three BRBs; and four combinations of these faults. The authors proposed using matching pursuit and DWT for feature extraction from current and vibration signals. The signals were acquired from an experimental array, where an eight-channel power quality analyzer was employed to measure three-phase currents, and an accelerometer 356A32 was utilized for the vibration measurements. For

comparative purposes, SVM, KNN, and ensemble were used, along with 17 different classifiers available in the Classification Learner toolbox of MATLAB. Within these options, fine KNN, bagged trees, subspace KNN, fine Gaussian SVM, and weighted KNN performed better, exhibiting accuracies near 100%. The authors observed that the number of features for the classification affects detection accuracy; in this work, a higher number of features provided the best results. This method is useful for comparative purposes.

In [82], a current and vibration analysis is performed based on different algorithms, such as Matching pursuit, DWT, invasive weed optimization algorithm, K-Nearest neighbor, SVM, random forest, genetic algorithm, receiver operation characteristic curve, and statistical analysis; these works compare these algorithms to detect 1 BRB, 5-BRB, 8-BRB, ball bearing fault, inner and outer bearing fault, and stator fault. Other works combine analysis of current, speed, and flux, such as the one presented in [83], or speed, current, and torque, such as the one in [84]. Another combination of physical variables is explored in [85], where authors analyze temperature and flux signals, and in [79], where current and flux signals are employed. Some of the latest works reported for fault detection based on analyzing more than one physical variable are presented in Table 5.

**Table 5.** Works based on combined analysis.

#### **5. Future Trends**

The evolution of fault detection techniques has brought interest to study methods based on less conventional types of physical variables. Even though current and vibration analyses remain the preferred types of signals for fault detection, other signals, such as flux and thermal images, have gained attention in recent years [76,84,85].

Works that propose the use of combined analysis, such as current and vibrations, are also very popular; usually, these take advantage of each physical variable and extract more information than using only one variable but with a more complex experimental array. These works commonly compare the results for various faults and detection methods.

Most of the works studied in this paper use AI-based methods, which reveal that no matter the physical variable employed for the analysis, these methods provide good results with accuracies superior than 90%.

In summary, current and vibration signals are commonly analyzed by their frequency spectrum using techniques, such as Fourier and wavelet transform, but their disadvantage is that both of them are prone to distortion, given the sensitivity of the sensor, for example, current signals by means of electrical noise and vibration signals by the vibrations or surrounding motors.

Some of the trends that have been observed during the elaboration of this work are listed below:

It is expected that this work could provide useful information for researchers to prefer a certain type of physical variable in the study of new methods for fault detection.

Finally, Table 6 summarizes a comparison of the characteristics of the different physical variables treated in this work.

**Table 6.** Comparison of methods for fault detection based on different physical variables.

#### **6. Conclusions**

Fault detection in IMs is a topic that has been studied using different physical variables of the motor, such as current, vibrations, flux, and temperature. Among them, current analysis remains the most popular one, mainly because of the non-invasive sensing and considering that the IMs have the current sensor already installed for control purposes [18,92–117]. A promising physical variable that has been employed for fault detection is flux analysis, which exhibits good results, even in cases where the MCSA fails; thus, it is expected that works based on flux analysis will grow in the next years.

Tendencies in processing techniques for analysis of the different types of physical variables treated in this work are inclined to use artificial-intelligence-based methods. Although these methods have been proposed in different works during the last ten years, advances in hardware resources for computers and software tools have made its implementation an easier task, making it more accessible to use in laptops and to implement in microcontrollers and FPGAs.

On the other hand, lower levels of damage are expected to be detected in the future. For instance, in current analysis for broken bar detection, most works report no less than a half broken bar. Therefore, improving these results by combining the information given by other variables, such as flux and current or temperature and vibrations, is a matter of interest.

Another interesting possibility is to automatize the sensing of flux signals, aiming to implement remote monitoring. New works on flux signal acquisition may represent an important improvement in the IM condition-monitoring field.