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A gearbox fault feature extraction method based on wingsuit flying search algorithm-optimized orthogonal matching pursuit with a compound time-frequency atom dictionary

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Abstract

Gearbox vibration signals are usually disturbed under strong noise. Therefore, the fault feature frequency cannot be extracted accurately and effectively. In order to extract the fault feature of the gearbox, a method based on compound time-frequency atomic dictionary and orthogonal matching pursuit (OMP) optimized by wingsuit flying search algorithm (WFSA) is proposed. Firstly, according to the feature of the gearbox fault vibration signal, a compound time-frequency atomic dictionary composed of steady state modulation dictionary and impact modulation dictionary is designed. In addition, in order to improve the accuracy and efficiency of signal sparse decomposition. In this paper, WFSA is used to optimize the dictionary atomic parameters in OMP, so that the dictionary atom approximated the original signal better. Through simulation analysis and experimental verification and compared with the commonly used gearbox fault feature extraction methods, the superiority and effectiveness of the method are verified.

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Acknowledgments

This research was supported by the National Natural Science Foundation of China (No.51975117) and Jiangsu provincial key research and development program (No. BE2019086).

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Authors and Affiliations

  1. School of Mechanical Engineering, Southeast University, Nanjing, 211189, China

    Yifan Mao, Feiyun Xu & Xun Zhao

  2. School of Mechatronics Engineering, Nanjing Forestry University, Nanjing, 210037, China

    Xiaoan Yan

Authors
  1. Yifan Mao
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  2. Feiyun Xu
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  3. Xun Zhao
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  4. Xiaoan Yan
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Corresponding author

Correspondence to Feiyun Xu.

Additional information

Yifan Mao was born in Shanxi Province, China in 1993. He is currently a Graduate Student in the School of Mechanical Engineering, Southeast University, China. His research interests are mainly gearbox fault diagnosis and signal processing.

Feiyun Xu received the B.Sc. degree in Industrial Electric Automation from Jilin University of Technology, Changchun, China, in 1991, the Ph.D. degree in Precision Instrumentation and Machinery from Southeast University, Nanjing, China, in 1996. Currently, he is a Professor at School of Mechanical Engineering, Southeast University, China. His research interest lies in intelligent testing and signal processing, dynamic system modeling and identification, status monitoring and intelligent fault diagnosis.

Xun Zhao received the M.S. degree from Southeast University, Nanjing, China, in 2019. He is currently working toward the Ph.D. degree with the School of Mechanical Engineering, Southeast University, Nanjing, China. His main research interests include computer vision, fault diagnosis, machine learning, and detection method of asphalt segregation.

Xiaoan Yan received the M.S. degree in Mechanical Engineering from North China Electric Power University, Baoding, China, in 2015 and the Ph.D. degree in Mechanical Engineering from Southeast University, Nanjing, China, in 2019. He is currently a Lecturer at School of Mechatronics Engineering, Nanjing Forestry University, Nanjing, China. His research interests include signal processing, intelligent fault diagnosis, pattern recognition, health status assessment and residual life prediction.

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Mao, Y., Xu, F., Zhao, X. et al. A gearbox fault feature extraction method based on wingsuit flying search algorithm-optimized orthogonal matching pursuit with a compound time-frequency atom dictionary. J Mech Sci Technol 35, 4825–4833 (2021). https://doi.org/10.1007/s12206-021-1002-5

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  • DOI: https://doi.org/10.1007/s12206-021-1002-5

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