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An Adaptive Fourier Decomposition Method for Gear Fault Diagnosis of Railway Vehicle in the Non-stationary Process

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Proceedings of the 6th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2023 (EITRT 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1137))

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Abstract

The vibration signal of railway vehicle gears has prominent non-stationary characteristics, and its feature extraction is a challenging issue for fault diagnosis. To tackle this issue, we proposed an adaptive Fourier decomposition method to extract the fault frequency characteristics in non-stationary signals. This method, combined with the computational order tracking technique, can eliminate the signal’s fast-changing characteristics and improve the algorithm performance obtaining intrinsic mode decomposition by taking advantage of the orthogonality, completeness, locality, and adaptability of the Fourier decomposition method (FDM). Then, a parameter setting-free AFDM method is established based on the reconstructing signal using FDM entropy and weighted kurtosis. Finally, the simulation signal and experimental signal study cases are carried out to verify its effectiveness. The results show that the proposed method’s peak signal-to-noise ratio (PSNR) of the reconstructed signal is higher 23.2% than the empirical mode decomposition method (EMD), which can effectively extract the weak sideband frequency features generated by the modulation of the impact behaviour, and demodulates the weak frequency to identify the gear spalling fault of the railway vehicle.

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Acknowledgements

The authors appreciate the support from the Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles for this research. This work was supported by the National Natural Science Foundation of China [51975038]; the Beijing Natural Science Foundation (3214042, KZ202010016025, L191005); the Beijing Postdoctoral Research Foundation (2021-zz-114); the Development of High-Level Teachers in Beijing Municipal Universities (CIT&TCD201904062); the Open Research Fund Program of Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles (PGU2020K001); the Fundamental Research Funds for the Beijing Municipal Universities (X21049).

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Authors and Affiliations

  1. School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China

    Zhongshuo Hu & Qiang Li

  2. School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China

    Jinhai Wang, Jianwei Yang & Dechen Yao

Authors
  1. Zhongshuo Hu
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  2. Qiang Li
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  3. Jinhai Wang
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  4. Jianwei Yang
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  5. Dechen Yao
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Corresponding author

Correspondence to Jinhai Wang .

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Editors and Affiliations

  1. Beijing Jiaotong University, Beijing, China

    Yong Qin

  2. Beijing Jiaotong University, Beijing, China

    Limin Jia

  3. Beijing University of Civil Engineering and Architecture, Beijing, China

    Jianwei Yang

  4. Beijing Jiaotong University, Beijing, China

    Lijun Diao

  5. Beijing University of Civil Engineering and Architecture, Beijing, China

    Dechen Yao

  6. University of Salford, Salford, UK

    Min An

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Hu, Z., Li, Q., Wang, J., Yang, J., Yao, D. (2024). An Adaptive Fourier Decomposition Method for Gear Fault Diagnosis of Railway Vehicle in the Non-stationary Process. In: Qin, Y., Jia, L., Yang, J., Diao, L., Yao, D., An, M. (eds) Proceedings of the 6th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2023. EITRT 2023. Lecture Notes in Electrical Engineering, vol 1137. Springer, Singapore. https://doi.org/10.1007/978-981-99-9311-6_60

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  • DOI: https://doi.org/10.1007/978-981-99-9311-6_60

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  • Publisher Name: Springer, Singapore

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  • Online ISBN: 978-981-99-9311-6

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