Abstract
Improving diagnostic efficiency and shortening diagnostic time is important for improving the reliability and safety of rotating machinery, and has received more and more attention. When using intelligent diagnostic methods to diagnose bearing faults, the increasingly complex working conditions and the huge amount of data make it a great challenge to diagnose fault quickly and effectively. In this paper, a novel fault diagnosis method based on sparse auto-encoder (SAE), combined with compression sensing (CS) and wavelet packet energy entropy (WPEE) for feature dimension reduction is proposed. Firstly, vibration signals of each fault type are projected linearly through compressed sensing to obtain compressed signals, which are merged into a low-dimensional compressed signal matrix of multiple fault types. Secondly, the WPEE of low-dimensional compressed signal matrix of multi-fault type is determined, and the eigenvector matrix of bearing fault diagnosis is formed, which greatly reduces the dimension of the eigenvector matrix. Finally, SAE are constructed by adding sparse penalty to auto-encoder (AE) for high-level feature learning and bearing fault classification, and it not only further learns the high-level features of data, but also reduces the feature dimension. Compared with traditional feature extraction methods and the standard deep learning method, the proposed method not only guarantees high accuracy, but also greatly reduces the diagnosis time.
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Acknowledgments
The studies were funded by the National Natural Science Foundation of China [Grant numbers 61973262 and 51875500], Natural Science Foundation of Hebei Province (Grant number E2019203146), and Hebei Province Graduate Innovation Funding Project [Grant number 2019000629].
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Peiming Shi received Ph.D. degree in information science and engineering institute from Yanshan University, Qinhuangdao, China, in 2009. Now he is a Professor in Institute of Electrical Engineering of Yanshan University. His current research interests include fault diagnosis and signal processing.
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Shi, P., Guo, X., Han, D. et al. A sparse auto-encoder method based on compressed sensing and wavelet packet energy entropy for rolling bearing intelligent fault diagnosis. J Mech Sci Technol 34, 1445–1458 (2020). https://doi.org/10.1007/s12206-020-0306-1
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DOI: https://doi.org/10.1007/s12206-020-0306-1