Journal
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
Volume 34, Issue 4, Pages 1445-1458Publisher
KOREAN SOC MECHANICAL ENGINEERS
DOI: 10.1007/s12206-020-0306-1
Keywords
Intelligent diagnosis; Rolling bearing; Compressed sensing; Sparse auto-encoder; Wavelet packet energy entropy
Categories
Funding
- National Natural Science Foundation of China [61973262, 51875500]
- Natural Science Foundation of Hebei Province [E2019203146]
- Hebei Province Graduate Innovation Funding Project [2019000629]
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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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