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Refined time-shift multiscale dispersion Lempel-Ziv complexity to diagnose rolling bearing faults

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Abstract

The key to damage detection is whether fault features can be extracted effectively from raw signals. Hence, we propose an approach based on the refined time-shift multiscale dispersion Lempel-Ziv complexity (RTSMDLZC) to effectively extract fault features. First, the time-shift multiscale sequence constructed from the raw time series can obtain more fault information more effectively. Then, the refined method addresses the lacking of sizeable numerical fluctuation on a large scale and enhances the algorithm’s stability. Simulation signals and two experimental cases verify the effectiveness and applicability of the RTSMDLZC. The results indicate that compared with other classic methods, the RTSMDLZC can extract bearing fault features more accurately and has better identification accuracy.

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Acknowledgments

This work is supported by the Natural Science Foundation of Sichuan Province (No. 2022JY0400). We would like to thank CWRU for offering bearing data.

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Authors and Affiliations

  1. School of Railway Tracks and Transportation, Wuyi University, Jiangmen, 529020, China

    Yongjian Li, Li Tan & Peng Li

  2. School of Intelligent Manufacturing and Automobile, Chengdu Vocational & Technical College of Industry, Chengdu, 610031, China

    Qing Xiong

Authors
  1. Yongjian Li
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  2. Li Tan
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  3. Peng Li
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  4. Qing Xiong
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Corresponding author

Correspondence to Peng Li.

Additional information

Yongjian Li received a Ph.D. degree in mechanical engineering from Southwest Jiaotong University, Chengdu, China, in 2017, he is currently a Lecturer at the School of Railway Tracks and Transportation, Wuyi University, China. His research interests include signal processing and data mining for machine health monitoring and fault diagnosis.

Peng Li is currently working as a Lecturer at the School of Railway Tracks and Transportation at Wuyi University. He received his Ph.D. in vehicle engineering from Central South University in 2017. His research interests mainly include machine vision and damage identification.

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Li, Y., Tan, L., Li, P. et al. Refined time-shift multiscale dispersion Lempel-Ziv complexity to diagnose rolling bearing faults. J Mech Sci Technol 37, 4557–4566 (2023). https://doi.org/10.1007/s12206-023-0812-z

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  • DOI: https://doi.org/10.1007/s12206-023-0812-z

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