1-D Residual Convolutional Neural Network coupled with Data Augmentation
  and Regularization for the ICPHM 2023 Data Challenge
In this article, we present our contribution to the ICPHM 2023 Data Challenge
on Industrial Systems' Health Monitoring using Vibration Analysis. For the task
of classifying sun gear faults in a gearbox, we propose a residual
Convolutional Neural Network that operates on raw three-channel time-domain
vibration signals. In conjunction with data augmentation and regularization
techniques, the proposed model yields very good results in a multi-class
classification scenario with real-world data despite its relatively small size,
i.e., with less than 30,000 trainable parameters. Even when presented with data
obtained from multiple operating conditions, the network is still capable to
accurately predict the condition of the gearbox under inspection.
Matthias Kreuzer, Walter Kellermann
D Residual Convolutional Neural Network coupled with Data Augmentation and Regularization for the ICPHM 2023 Data Challenge

###### Abstract

In this article, we present our contribution to the International Conference on Prognostics and Health Management (ICPHM) 2023 Data Challenge on Industrial Systems' Health Monitoring using Vibration Analysis. For the task of classifying sun gear faults in a gearbox, we propose a residual Convolutive Neural Network (CNN) that operates on raw three-channel time-domain vibration signals. In conjunction with data augmentation and regularization techniques, the proposed model yields very good results in a multi-class classification scenario with real-world data despite its relatively small size, i.e., with less than 30,000 trainable parameters. Even when presented with data obtained from multiple operating conditions, the network is still capable to accurately predict the condition of the gearbox under inspection.

 IPCHM 2023 Data Challenge, Vibration Analysis, Condition Monitoring, Fault Classification, Planetary Gearbox

## I Introduction

Industrial machines are subjected to heavy stress conditions and are therefore susceptible to defects and resulting malfunctions. Even small defects can already have significant consequences as they can cause additional costs by, e.g., delaying industrial production through unexpected downtime, or can even put human lives in danger, e.g., resulting from bearing failure in rail vehicles. These damages can stem from normal wear and tear, from poor maintenance or completely unforeseen events. To detect such damages in an early stage whenever possible, reliable condition monitoring techniques are of utmost importance. However, the manual inspection of machines proves difficult and cost-intensive and therefore automatic non-intrusive condition monitoring techniques are preferred and highly sought after. To this end, the analysis of vibration signals has proven to be especially effective since faults especially in rotating machinery alter the typical vibration pattern of a machine. These alterations in the vibration signature can be detected by applying signal processing techniques, e.g., envelope spectrum analysis [1], signal decomposition and filtering techniques [2], or by manually extracting fault-related features [3][4]. However, these traditional methods have recently been eclipsed by end-to-end deep learning-based approaches [5, 6, 7, 8, 9]. Although Deep Neural Networks (DNNs) have proven to be very powerful for the task of fault detection, they come with certain drawbacks. DNN-based approaches require a sufficiently large amount of training data for performing well. Yet, obtaining a sufficient amount of data, especially for faulty conditions is often difficult. Moreover, many architectures rely on a large number of parameters, which makes the training process and the usage with on-board devices difficult. If the number of parameters is too large and the amount of training data insufficient, these networks tend to overfit and not to generalize well for unseen test data. To avoid these drawbacks, we propose a residual Convolutive Neural Network (CNN) that stands out for its small number of parameters and additonally employ data augmentation techniques to further mitigate the effect of a possible over-fitting for the ICPHM 2023 Data Challenge on Industrial Systems' Health Monitoring using Vibration Signal Analysis [10].

This paper is structured as follows: In Sec. II, the dataset of the ICPHM 2023 Data Challenge is described. The proposed fault detection approach is then presented in Sec. III. In particular, the proposed network architecture is discussed in III-A, whereas the utilized data augmentation techniques and the training procedure are described in Sec. III-B and Sec. III-C, respectively. After this, the performance of the model is evaluated in Sec. IV before conclusions are drawn in Sec. V.