Journal
KNOWLEDGE-BASED SYSTEMS
Volume 140, Issue -, Pages 1-14Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2017.10.024
Keywords
Intelligent fault diagnosis; Rolling bearing; Deep wavelet auto-encoder; Extreme learning machine; Unsupervised feature learning
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Funding
- National Natural Science Foundation of China [51475368]
- Shanghai Engineering Research Center of Civil Aircraft Health Monitoring Foundation of China [GCZX-2015-02]
- Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University [CX201710]
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Unsupervised feature learning from the raw vibration data is a great challenge for rolling bearing intelligent fault diagnosis. In this paper, a novel method called deep wavelet auto-encoder (DWAE) with extreme learning machine (ELM) is proposed for intelligent fault diagnosis of rolling bearing. Firstly, wavelet function is employed as the nonlinear activation function to design wavelet auto-encoder (WAE), which can effectively capture the signal characteristics. Secondly, a DWAE is constructed with multiple WAEs to enhance the unsupervised feature learning ability. Finally, ELM is adopted as the classifier to accurately identify different bearing faults. The proposed method is applied to analyze the experimental bearing vibration signals, and the results confirm that the proposed method is superior to the traditional methods and standard deep learning methods. (C) 2017 Elsevier B.V. All rights reserved.
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