Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network

Hongkai Jiang, Xingqiu Li, Haidong Shao, Ke Zhao

科研成果: 期刊稿件文章同行评审

165 引用 (Scopus)

摘要

Traditional intelligent fault diagnosis methods for rolling bearings heavily depend on manual feature extraction and feature selection. For this purpose, an intelligent deep learning method, named the improved deep recurrent neural network (DRNN), is proposed in this paper. Firstly, frequency spectrum sequences are used as inputs to reduce the input size and ensure good robustness. Secondly, DRNN is constructed by the stacks of the recurrent hidden layer to automatically extract the features from the input spectrum sequences. Thirdly, an adaptive learning rate is adopted to improve the training performance of the constructed DRNN. The proposed method is verified with experimental rolling bearing data, and the results confirm that the proposed method is more effective than traditional intelligent fault diagnosis methods.

源语言英语
文章编号065107
期刊Measurement Science and Technology
29
6
DOI
出版状态已出版 - 10 5月 2018

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