Title: Research on failure identification of mechanical parts of automobile transmission based on deep reinforcement learning

Authors: Heyang Guo; Qiong Li

Addresses: Department of Mechanical and Electrical Engineering, Luohe Vocational Technology College, Henan, Luohe, 462002, China ' Department of Mechanical and Electrical Engineering, Luohe Vocational Technology College, Henan, Luohe, 462002, China

Abstract: Aiming at the problems of low accuracy and slow speed of traditional method, this paper proposes failure identification method of mechanical parts of automobile transmission based on deep reinforcement learning. Firstly, the failure factors of mechanical gears and bearings in automobile transmissions are analysed, and the failure data are extracted. Then, the mean value, skewness value and kurtosis value of the dimensional parameters of the mechanical part failure fault are calculated, and the pre-processing of the part failure data is realised. Finally, the weight and offset value of the failure data are calculated, the failure data tag is constructed, the loss function through in-depth reinforcement learning is determined, and the failure point of mechanical parts is determined by searching for the failure reward value of mechanical parts to complete the failure identification. Experimental results show that the proposed method can effectively improve the accuracy of failure identification of mechanical parts, and the speed is fast.

Keywords: intensive learning; automobile transmission; mechanical parts; gear; time domain statistical analysis; loss function.

DOI: 10.1504/IJCISTUDIES.2023.132496

International Journal of Computational Intelligence Studies, 2023 Vol.12 No.1/2, pp.105 - 117

Received: 14 Nov 2022
Accepted: 07 Mar 2023
Published online: 24 Jul 2023 *

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