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

Hongkai Jiang, Xingqiu Li, Haidong Shao, Ke Zhao

Research output: Contribution to journalArticlepeer-review

165 Scopus citations

Abstract

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.

Original languageEnglish
Article number065107
JournalMeasurement Science and Technology
Volume29
Issue number6
DOIs
StatePublished - 10 May 2018

Keywords

  • adaptive learning rate
  • deep learning
  • improved deep recurrent neural network
  • intelligent fault diagnosis
  • rolling bearing

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