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



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


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.

Abstract 153 | PDF Downloads 139



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

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