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Intelligent detection of rail corrugation using ACMP-based energy entropy and LSSVM

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Abstract

In this paper, an intelligent method to diagnose rail corrugation based on signal decomposition and entropy theory is proposed. The axle box acceleration signals are first decomposed into several components with different frequency bands by ACMP, EEMD and MODWT. By comparison, ACMP is able to successfully extract rail corrugation component from original signal without mode mixing. Energy entropy is then introduced here to quantify the degree of the rate of energy concentration. The analysis results show that the energy will change when rail corrugation occurs and the entropy will become small. It has been also proved that the entropy difference of rail corrugation and normal signal based on ACMP is the most significant. In addition, to intelligently diagnose rail corrugation, we combine energy entropy with energy index and the first mode energy, regarded as the input feature vector of LSSVM, to distinguish rail corrugation from mass data sets. It is obvious that the accuracy of ACMP-based technique is the highest.

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Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.

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Acknowledgements

The financial supports from the funds of the Fundamental Research Funds for the Central Universities (2021YJS166), the funds of China Academy of Railway Science Cooperation Limited (2019YJ153) and the National Natural Science Foundation of China (62171018) are gratefully acknowledged.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Beijing Jiaotong University, Beijing, 100044, People’s Republic of China

    Sange Li & Pengjian Shang

  2. Infrastructure Inspection Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing, 100081, People’s Republic of China

    Xuegeng Mao, Xiaodi Xu & Jinzhao Liu

  3. Beijing IMAP Technology Corporation Limited, Beijing, 100081, People’s Republic of China

    Peng Qiao

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  1. Sange Li
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  2. Xuegeng Mao
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  3. Pengjian Shang
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  4. Xiaodi Xu
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Correspondence to Xuegeng Mao.

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Li, S., Mao, X., Shang, P. et al. Intelligent detection of rail corrugation using ACMP-based energy entropy and LSSVM. Nonlinear Dyn 111, 8419–8438 (2023). https://doi.org/10.1007/s11071-022-08066-2

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