Diagnosing Automotive Damper Defects Using Convolutional Neural Networks and Electronic Stability Control Sensor Signals

Thomas Zehelein,Thomas Hemmert-Pottmann,Markus Lienkamp

doi:10.3390/jsan9010008

Thomas Zehelein, Thomas Hemmert-Pottmann + Show 1 more

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https://doi.org/10.3390/jsan9010008

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Abstract

Chassis system components such as dampers have a significant impact on vehicle stability, driving safety, and driving comfort. Therefore, monitoring and diagnosing the defects of these components is necessary. Currently, this task is based on the driver’s perception of component defects in series production vehicles, even though model-based approaches in the literature exist. As we observe an increased availability of data in modern vehicles and advances in the field of deep learning, this paper deals with the analysis of the performance of Convolutional Neural Networks (CNN) for the diagnosis of automotive damper defects. To ensure a broad applicability of the generated diagnosis system, only signals of a classic Electronic Stability Control (ESC) system, such as wheel speeds, longitudinal and lateral vehicle acceleration, and yaw rate, were used. A structured analysis of data pre-processing and CNN configuration parameters were investigated in terms of the defect detection result. The results show that simple Fast Fourier Transformation (FFT) pre-processing and configuration parameters resulting in small networks are sufficient for a high defect detection rate.

Highlights

Ensuring driving safety and driving comfort when operating vehicles requires their health state to be properly monitored
As we observe an increased availability of data in modern vehicles and advances in the field of deep learning, this paper deals with the analysis of the performance of Convolutional Neural Networks (CNN) for the diagnosis of automotive damper defects
The results show that simple Fast Fourier Transformation (FFT) pre-processing and configuration parameters resulting in small networks are sufficient for a high defect detection rate

Summary

Introduction

Ensuring driving safety and driving comfort when operating vehicles requires their health state to be properly monitored. This is especially critical for chassis system components such as dampers. In addition to the driver’s perception, there is only periodic human inspection for monitoring the vehicle’s chassis system health state. This is error-prone, expensive, and implies periods of unmonitored driving between inspections. Autonomous driving implies that the driver is not needed as a monitoring instance, either for the actual driving task or for monitoring the vehicle’s health state.

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