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Research on Switch Machine Fault Diagnosis Based on Wavelet Transform and CNN-LSTM

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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 1136))

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Abstract

The turnout is usually classified as the infrastructure in the railway system, whose safety and reliability are directly related to the stable operation of the railway system by its vital role. In this paper, we use the switch machine power curve recorded in the centralized signaling monitoring (CSM) system to conduct fault diagnosis. First, we obtain the mean value and difference value through wavelet transform as the input of convolutional neural network (CNN), realizing the extraction of switch machine fault features, and then use the extracted fault features as the input of long and short time memory network (LSTM) to finally realize switch machine fault diagnosis. The data is divided into training set and test set for training and testing the model. After 50 iterations, the accuracy of switch machine fault diagnosis reached 96.7% through experimental simulation.

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Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Haiwei Liu, Shiwu Yang, Shuo Liu & Ruoshui Liu

  2. Institute of Computing Technology, Academy of Railway Sciences Corporation Limited, Beijing, 100081, China

    Chang Liu

Authors
  1. Haiwei Liu
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  2. Shiwu Yang
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  3. Chang Liu
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  4. Shuo Liu
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  5. Ruoshui Liu
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Corresponding author

Correspondence to Shiwu Yang .

Editor information

Editors and Affiliations

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

    Jianwei Yang

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

    Dechen Yao

  3. Beijing Jiaotong University, Beijing, China

    Limin Jia

  4. Beijing Jiaotong University, Beijing, China

    Yong Qin

  5. Beijing Jiaotong University, Beijing, China

    Zhigang Liu

  6. Beijing Jiaotong University, Beijing, China

    Lijun Diao

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© 2024 Beijing Paike Culture Commu. Co., Ltd.

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Liu, H., Yang, S., Liu, C., Liu, S., Liu, R. (2024). Research on Switch Machine Fault Diagnosis Based on Wavelet Transform and CNN-LSTM. In: Yang, J., Yao, D., Jia, L., Qin, Y., Liu, Z., Diao, L. (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 1136. Springer, Singapore. https://doi.org/10.1007/978-981-99-9315-4_21

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  • DOI: https://doi.org/10.1007/978-981-99-9315-4_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9314-7

  • Online ISBN: 978-981-99-9315-4

  • eBook Packages: EngineeringEngineering (R0)

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