Deep Learning Approach for Operational Transfer Path Analysis: Case Study of Electric Vehicles

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Published Sep 12, 2023
Jeongmin Oh Donghwi Yoo Hyunseok Oh Yong Hyun Ryu Kyung-Woo Lee Dae-Un Sung

Abstract

This paper presents a new approach to fault diagnosis of the drivetrains of the electric vehicle. Most commercially available electric vehicles do not have accelerometers on electric drivetrains making it difficult to detect fault characteristics of the drivetrains of the electric vehicle, whereas accelerometers exist on the driver's seat. The proposed approach’s key idea is based on the operational transfer path analysis that determines the transfer function between the source and receiver. The transfer function is derived by training a deep learning model. The deep learning model converts the driver's seat vibration signals into drivetrains vibration signals. The validity of the proposed approach is evaluated using data from the durability test of real electric vehicles. It is anticipated that the proposed approach is effective to diagnose electric vehicle drivetrains subjected to external noise conditions.

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Keywords

Electric vehicle, Fault diagnosis, Operational transfer path analysis, Convolutional neural network

References
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Cheng, W., Blamaud, D., Chu, Y., Meng, L., Lu, J., & Basit, W. A. (2020). Transfer Path Analysis and Contribution Evaluation Using SVD- and PCABased Operational Transfer Path Analysis. Shock and Vibration, Vol. 2020, Article number 9673838.
Section
Special Session Papers