In-wheel motor fault diagnosis method based on BN and improved DST

被引：0
|
作者：
Li Z. [1 ]
Qin X. [1 ]
Xue H. [1 ]
机构：
[1] School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang
来源：
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2021年 / 49卷 / 08期
关键词：
Bayesian network; Dempster-Shafer evidence theory; Fault diagnosis; In-wheel motor; Vibration and noise;
D O I：
10.13245/j.hust.210805
中图分类号：
学科分类号：
摘要：
In order to realize in-wheel motor fault diagnosis under different signal sources, an in-wheel motor fault diagnosis method based on Bayesian network (BN) and improved DST (Dempster-Shafer evidence theory) was proposed. The BN diagnostic model based on vibration and noise was firstly built for the operation safety of electric vehicle in-wheel motor. Secondly, the posterior probability of BN diagnosis model based on vibration and noise under different operating conditions was fused. Then an improved DST based on entropy weight was proposed to reallocate the conflicting parts of the posterior probability of BN diagnostic model based on vibration and noise, and a new basic reliability function was obtained. Finally, the effectiveness of the method was verified with in-wheel motor bench test. It is shown that improved Dempster-Shafer evidence theory can effectively solve the problem of inter-evidence conflict and integrate the BN diagnosis posterior probability based on vibration and noise to realize in-wheel motor fault diagnosis. © 2021, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.
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页码：27 / 32
页数：5
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