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An effective multi-channel fault diagnosis approach for rotating machinery based on multivariate generalized refined composite multi-scale sample entropy

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Abstract

Fault diagnosis of critical rotating machinery components is necessary to ensure safe operation. However, the commonly used rotating machinery fault diagnosis methods are generally based on the single-channel signal processing method, which is not suitable for processing multi-channel signals. Thus, to extract features and carry out the intelligent diagnosis of multi-channel signals, a novel method for rotating machinery fault diagnosis is proposed. Firstly, a novel nonlinear dynamics technique named the multivariate generalized refined composite multi-scale sample entropy was presented and applied to extract fusion entropy features of multi-channel signals. Secondly, a practical manifold learning known as supervised isometric mapping was introduced to map the high-dimensional fusion entropy features in a low-dimensional space. In a third step, the Harris hawks optimization-based support vector machine was applied to carry out the intelligent fault recognition. Finally, aiming to verify the effectiveness of the proposed method and present its advantages, it was applied to analyze the rotating machinery system bearing and gear data. The experimental results have shown that the method at hand can accurately identify various faults in both the bearings and gears. Furthermore, in addition to being suitable for multi-channel signal fault diagnosis, it had higher recognition accuracy compared to other multi-channel or single-channel methods.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant No. 51775114, 51875105, 51275092); the Fujian Provincial Industrial Robot Basic Components Technology Research and Development Center (Grant No. 2014H21010011); and the Natural Science Foundation of Anhui Province (Grant No. 1808085ME152).

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Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, People’s Republic of China

    Zhenya Wang, Hui Chen, Ligang Yao & Jun Zhang

  2. School of Mechanical Engineering, Anhui University of Technology, Maanshan, 243032, People’s Republic of China

    Xu Chen & Xiaoli Qi

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  1. Zhenya Wang
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  3. Ligang Yao
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Correspondence to Ligang Yao or Jun Zhang.

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Wang, Z., Chen, H., Yao, L. et al. An effective multi-channel fault diagnosis approach for rotating machinery based on multivariate generalized refined composite multi-scale sample entropy. Nonlinear Dyn 106, 2107–2130 (2021). https://doi.org/10.1007/s11071-021-06827-z

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