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Research on sparsity measures for rotating machinery health monitoring

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Abstract

Machine health management is one of the main research contents of PHM technology, which aims to monitor the health states of machines online and evaluate degradation stages through real-time sensor data. In recent years, classic sparsity measures such as kurtosis, Lp/Lq norm, pq-mean, smoothness index, negative entropy, and Gini index have been widely used to characterize the impulsivity of repetitive transients. Since smoothness index and negative entropy were proposed, the sparse properties have not been fully analyzed. The first contribution of this paper is to analyze six properties of smoothness index and negative entropy. In addition, this paper conducts a thorough investigation on multivariate power average function and finds that existing classical sparsity measures can be respectively reformulated as the ratio of multivariate power mean functions (MPMFs). Finally, a general paradigm of index design is proposed for the expansion of sparsity measures family, and several newly designed dimensionless health indexes are given as examples. Two different run-to-failure bearing datasets were used to analyze and validate the capabilities and advantages of the newly designed health indexes. Experimental results prove that the newly designed health indexes show good performance in terms of monotonic degradation description, first fault occurrence time determination and degradation state assessment.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant No.52075095). Comments and suggestions from the editor and reviewers are very much appreciated.

Author information

Authors and Affiliations

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

    Yudong Cao, Minping Jia & Jichao Zhuang

  2. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Xiaoli Zhao

Authors
  1. Yudong Cao
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  2. Minping Jia
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  3. Jichao Zhuang
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  4. Xiaoli Zhao
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Corresponding author

Correspondence to Minping Jia.

Additional information

Yudong Cao received the B.Eng. degree in mechatronic engineering from the School of Mechanical Engineering, Jiangnan University, Wuxi, China, in 2019, and the M.Eng. degree in mechanical engineering from Southeast University, Nanjing, China, in 2022. He is currently working toward the Ph.D. degree. His research interests include machine status monitoring and fault diagnosis, prognostic and health management for rotating machinery, intelligence systems, and machine learning.

Minping Jia received the B.S. and M.S. degrees from the Nanjing Institute of Technology (now Southeast University), Nanjing, China, in 1982 and 1985, respectively, and the Ph.D. degree from Southeast University, Nanjing, China, in 1991, all in mechanical engineering. He is currently a Full Professor with Southeast University, Nanjing, China. His research interests include dynamic signal processing, machine fault diagnosis, and vibration engineering applications.

Jichao Zhuang received the B.S. degree from Guangdong Ocean University, Zhanjiang, China, in 2018, and the M.S. degree from Yanshan University, Qinhuangdao, China, in 2020. He is currently pursuing the Ph.D. degree with the School of Mechanical Engineering, Southeast University, Nanjing, China. His research interests include machine fault diagnosis and dynamic signal processing.

Xiaoli Zhao received his M.S. degree from LanZhou University of Technology, Lanzhou, China, in 2017. He received the Ph.D. degree in Mechanical engineering from School of Mechanical Engineering, Southeast University, Nanjing, P. R. China, in 2021. From September 2019 to September 2020, he was also as a visiting Ph.D. scholar in the School of Engineering, the University of British Columbia, Okanagan, Canada. Now, he is currently an Assistant Professor in School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, P. R. China. His main research interest is intelligent monitoring and fault diagnosis, Prognostic and Health Management for electromechanical and hydraulic system, artificial intelligence and signal processing, robot technology.

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Cao, Y., Jia, M., Zhuang, J. et al. Research on sparsity measures for rotating machinery health monitoring. J Mech Sci Technol 36, 5831–5843 (2022). https://doi.org/10.1007/s12206-022-1102-x

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  • DOI: https://doi.org/10.1007/s12206-022-1102-x

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