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

作者：

Highlights：

• Wavelet auto-encoder is designed with wavelet function to capture the signal characteristics.

• A deep wavelet auto-encoder is constructed with multiple wavelet auto-encoders to enhance the unsupervised feature learning ability.

• The proposed method effectively diagnoses the different fault types, different fault severities and different fault orientations of rolling bearing.

摘要

•Wavelet auto-encoder is designed with wavelet function to capture the signal characteristics.•A deep wavelet auto-encoder is constructed with multiple wavelet auto-encoders to enhance the unsupervised feature learning ability.•The proposed method effectively diagnoses the different fault types, different fault severities and different fault orientations of rolling bearing.

论文关键词：Intelligent fault diagnosis,Rolling bearing,Deep wavelet auto-encoder,Extreme learning machine,Unsupervised feature learning

论文评审过程：Received 22 May 2017, Revised 20 August 2017, Accepted 20 October 2017, Available online 23 October 2017, Version of Record 6 December 2017.

论文官网地址：https://doi.org/10.1016/j.knosys.2017.10.024