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Journal of Automotive Safety and Energy ›› 2023, Vol. 14 ›› Issue (2): 232-238.DOI: 10.3969/j.issn.1674-8484.2023.02.010

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Fault diagnosis of vehicle motor-bearings under safe running by digital-twin technology

ZHANG Xi1(), LIAO Yulan2(), LI Qinyi1,3, CHEN Yiqing1,3   

  1. 1. Guang’an Vocational Technical College, Guang’an 638000, China
    2. College of Electromechanical Engineering, Hainan University, Haikou 570100, China
    3. Rangsit University, Baton Tani 12000, Thailand
  • Received:2022-10-09 Revised:2022-12-13 Online:2023-04-30 Published:2023-04-27

Abstract:

To ensure the driving safety of automobiles and timely capture the fault status of motor bearings, a fault diagnosis of motor bearings was completed through the combination of deep learning and digital twin technology. The detailed attributes of the bearings were transformed into virtual space through digital twin technology, and a digital twin body with shape, attributes, criteria, and behavior was constructed. The shape and attribute modules integrated the object parameters of the motor bearings to build a criterion module based on fault signal processing, diagnosis, and classification, and combined the historical records injected by the behavior module to generate data for network training. Data analysis was completed using Matlab 2020a. The results show that the diagnostic accuracies of this method for conventional signals, inner race fault, outer race fault, and rolling element fault are 97.5%, 97.8%, 96.8%, and 97.1%, respectively. Therefore, this method has superior diagnostic effect and algorithmic performance compared with that by using the methods of the hybrid neural network and the migrated residual network.

Key words: automotive safety, motor bearing, fault diagnosis, digital twin, deep learning, virtual space

CLC Number: