Next Article in Journal
Human Movement Monitoring and Analysis for Prehabilitation Process Management
Previous Article in Journal
Acknowledgement to Reviewers of Journal of Sensor and Actuator Networks in 2019
Previous Article in Special Issue
V2X Communications Applied to Safety of Pedestrians and Vehicles
Open AccessArticle

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

Institute of Automotive Technology, Technical University of Munich, Boltzmannstr. 15, 85748 Garching, Germany
*
Author to whom correspondence should be addressed.
J. Sens. Actuator Netw. 2020, 9(1), 8; https://doi.org/10.3390/jsan9010008
Received: 11 December 2019 / Revised: 7 January 2020 / Accepted: 10 January 2020 / Published: 16 January 2020
(This article belongs to the Special Issue Advances in Vehicular Networks)
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 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.
Keywords: automotive; damper; convolutional neural networks; fault detection; diagnosis; machine learning; deep learning automotive; damper; convolutional neural networks; fault detection; diagnosis; machine learning; deep learning
MDPI and ACS Style

Zehelein, T.; Hemmert-Pottmann, T.; Lienkamp, M. Diagnosing Automotive Damper Defects Using Convolutional Neural Networks and Electronic Stability Control Sensor Signals. J. Sens. Actuator Netw. 2020, 9, 8.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop