Deep convolutional neural networks for cyclic sensor data
Predictive maintenance plays a critical role in ensuring the uninterrupted
operation of industrial systems and mitigating the potential risks associated
with system failures. This study focuses on sensor-based condition monitoring
and explores the application of deep learning techniques using a hydraulic
system testbed dataset. Our investigation involves comparing the performance of
three models: a baseline model employing conventional methods, a single CNN
model with early sensor fusion, and a two-lane CNN model (2L-CNN) with late
sensor fusion. The baseline model achieves an impressive test error rate of 1%
by employing late sensor fusion, where feature extraction is performed
individually for each sensor. However, the CNN model encounters challenges due
to the diverse sensor characteristics, resulting in an error rate of 20.5%. To
further investigate this issue, we conduct separate training for each sensor
and observe variations in accuracy. Additionally, we evaluate the performance
of the 2L-CNN model, which demonstrates significant improvement by reducing the
error rate by 33% when considering the combination of the least and most
optimal sensors. This study underscores the importance of effectively
addressing the complexities posed by multi-sensor systems in sensor-based
condition monitoring.
Payman Goodarzi, Yannick Robin, Andreas Schütze, Tizian Schneider
# Deep convolutional neural networks for cyclic sensor data

###### Abstract

Predictive maintenance plays a critical role in ensuring the uninterrupted operation of industrial systems and mitigating the potential risks associated with system failures. This study focuses on sensor-based condition monitoring and explores the application of deep learning techniques using a hydraulic system testbed dataset. Our investigation involves comparing the performance of three models: a baseline model employing conventional methods, a single CNN model with early sensor fusion, and a two-lane CNN model (2L-CNN) with late sensor fusion. The baseline model achieves an impressive test error rate of 1% by employing late sensor fusion, where feature extraction is performed individually for each sensor. However, the CNN model encounters challenges due to the diverse sensor characteristics, resulting in an error rate of 20.5%. To further investigate this issue, we conduct separate training for each sensor and observe variations in accuracy. Additionally, we evaluate the performance of the 2L-CNN model, which demonstrates significant improvement by reducing the error rate by 33% when considering the combination of the least and most optimal sensors. This study underscores the importance of effectively addressing the complexities posed by multi-sensor systems in sensor-based condition monitoring.

 Predictive maintenance, hydraulic system, deep learning, convolutional neural network

## I Introduction

Industrial systems and factories operate continuously, necessitating uninterrupted performance to avoid process downtime, significant financial losses, and potential safety hazards. To mitigate these risks, companies employ various maintenance approaches, including corrective maintenance, preventive maintenance, and predictive maintenance. Predictive maintenance (PdM) heavily relies on monitored signals from diverse sensors, with machine learning methods playing a pivotal role in data-driven PdM. These methods can be categorized into two groups: conventional approaches and deep learning techniques. Conventional methods involve preprocessing, feature extraction (FE), feature selection (FS), and the subsequent application of classification or regression algorithms [1], commonly referred to as FESC/FESR in this study. In contrast, modern deep neural networks have demonstrated exceptional performance across various applications, including PdM [2, 3, 4].

In line with these advancements, gas mixture measurement has emerged as a promising application for deep neural networks in recent research. Notably, Robin et al. [5] introduced a convolutional neural network (CNN) specifically designed for indoor air quality monitoring (TCOCNN), accurately predicting volatile organic compounds using temperature-cycled operation sensors. The proposed method surpassed existing data evaluation techniques, underscoring the effectiveness of CNNs in this domain. It is worth noting that the signals utilized in their study bear resemblance to the typical data encountered in condition monitoring and predictive maintenance applications, i.e. multiple sensors with periodic or cyclic data.

Motivated by the aforementioned findings, the objective of our study is to compare our previously published method, TCOCNN, with a benchmark method in a different application context. To achieve this, we utilize a publicly available dataset from a hydraulic system testbed [6]. Recent research has applied various deep learning techniques to the dataset under investigation [7, 8, 9, 10, 11]. Prakash et al. [7] employed a 1D CNN model to analyze the pressure difference between two pressure sensors. Huang et al. [12] took a parallel approach by utilizing multiple independent convolutional neural networks to extract features from individual sensors. Furthermore, Berghout et al. [10] introduced a novel neural network model specifically designed to process the extracted features. In a distinct approach, Zhang et al. [9] demonstrated the application of a Transformer model with self-attention, originally trained on natural language, to the task of sensor fusion. Collectively, these studies contribute to the exploration of diverse methodologies for analyzing sensor data and extracting meaningful insights. The primary goal of our comprehensive evaluation is to assess the performance of the air quality model when applied to the field of condition monitoring. In doing so, we aim to address potential challenges and difficulties associated with multiple