Volume 23 Issue 1
Feb.  2023
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2023  >  23(1): 170-183
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WANG Jun, WANG Yu-qi, XUAN Jian-ping, LIU Jin-zhao, HUANG Wei-guo, ZHU Zhong-kui. Fault diagnosis method of vehicle transmission system based on manifold fusion of parameter-varying wavelet[J]. Journal of Traffic and Transportation Engineering, 2023, 23(1): 170-183. doi: 10.19818/j.cnki.1671-1637.2023.01.013
Citation: WANG Jun, WANG Yu-qi, XUAN Jian-ping, LIU Jin-zhao, HUANG Wei-guo, ZHU Zhong-kui. Fault diagnosis method of vehicle transmission system based on manifold fusion of parameter-varying wavelet[J]. Journal of Traffic and Transportation Engineering, 2023, 23(1): 170-183. doi: 10.19818/j.cnki.1671-1637.2023.01.013
shu

Fault diagnosis method of vehicle transmission system based on manifold fusion of parameter-varying wavelet

doi: 10.19818/j.cnki.1671-1637.2023.01.013
  • 1.

    School of Rail Transportation, Soochow University, Suzhou 215131, Jiangsu, China

  • 2.

    School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China

  • 3.

    Infrastructure Inspection Research Institute, China Academy of Railway Sciences Co., Ltd., Beijing 100081, China

Funds:

National Key Research and Development Program of China 2020YFB2007700

National Natural Science Foundation of China 52275121

National Natural Science Foundation of China 51805342

National Natural Science Foundation of China 52075353

China Postdoctoral Science Foundation 2021M692354

More Information
  • Author Bio:

    WANG Jun(1987-), male, associate professor, PhD, junking@suda.edu.cn

    HUANG Wei-guo(1981-), male, professor, PhD, wghuang@suda.edu.cn

  • Received Date: 2022-10-09
    Available Online: 2023-03-08
  • Publish Date: 2023-02-25
    Abstract
  • The manifold learning method was utilized to nonlinearly fuse the wavelet envelopes corresponding to the central scale under different wavelet parameters. The problem of effective extraction of the fault transient impulse envelopes from the vibration signals in vehicle transmission systems under heavy background noise was studied, and a comparative study with the traditional time-frequency methods for signal decomposition was carried out. Different wavelet parameters were adopted for the vibration signals to perform continuous wavelet transform (CWT), and the wavelet envelope corresponding to the central scale under each group of wavelet parameters was extracted. Some wavelet envelopes containing the information of the fault transient impulses were selected by Gini index, and the high-dimensional matrix of wavelet envelopes was constructed. The high-dimensional wavelet envelopes were fused based on manifold by using the local tangent space alignment (LTSA) algorithm, and the wavelet envelope manifold reflecting the intrinsic structure of fault transient impulse envelopes was obtained. In order to verify the effectiveness and superiority of the proposed method, the fault vibration signals of a railway-vehicle-wheelset bearing and an automobile change-speed gearbox were analyzed comparatively by different methods. Research results indicate that, compared to the traditional time-frequency methods for signal decomposition, the proposed method can improve the Gini index by over 27.32% in the case of bearing signal with an outer-race fault, and by over 26.74% in the case of gearbox signal with a wearing fault. It can be seen that, by synthesizing the parameter-varying wavelet envelopes with different patterns, the proposed method is well adaptive to the vibration signals for the faults of different key parts in the vehicle transmission systems with no need for wavelet parameter optimization. The extracted envelopes of the fault transient impulses have less in-band noise and distinct fault impulsive features. These advantages are beneficial to the identification of the fault characteristic frequencies in the spectra of the extracted envelopes. Therefore, the proposed method is an effective method for the fault detection of vehicle transmission systems.

     

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