Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine

Shao Haidong, Jiang Hongkai, Li Xingqiu, Wu Shuaipeng

科研成果: 期刊稿件文章同行评审

306 引用 (Scopus)

摘要

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.

源语言英语
页(从-至)1-14
页数14
期刊Knowledge-Based Systems
140
DOI
出版状态已出版 - 15 1月 2018

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