Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing

作者：

Highlights：

• Complex wavelet packet energy moment entropy is defined as the new monitoring index.

• A deep gated recurrent unit network is constructed to model nonlinear time series.

• A modified training algorithm is developed to further enhance the capability.

• Several cases are used to verify the effectiveness of the proposed method.

摘要

•Complex wavelet packet energy moment entropy is defined as the new monitoring index.•A deep gated recurrent unit network is constructed to model nonlinear time series.•A modified training algorithm is developed to further enhance the capability.•Several cases are used to verify the effectiveness of the proposed method.

论文关键词：Enhanced deep gated recurrent unit,Bearing,Early fault prognosis,Energy moment entropy,Modified training algorithm

论文评审过程：Received 28 January 2019, Revised 21 June 2019, Accepted 4 September 2019, Available online 6 September 2019, Version of Record 20 January 2020.

论文官网地址：https://doi.org/10.1016/j.knosys.2019.105022