A novel weak fault diagnosis method for rolling bearings based on LSTM considering quasi-periodicity

作者：

Highlights：

• The quasi-periodicity is considered in the proposed weak fault diagnosis method.

• A multi-channel representation method is proposed to keep the details in signal.

• Residual blocks were used to interpret the local time–frequency characteristics.

• The efficiency was validated by simulated fault bearings and real fault bearings.

摘要

•The quasi-periodicity is considered in the proposed weak fault diagnosis method.•A multi-channel representation method is proposed to keep the details in signal.•Residual blocks were used to interpret the local time–frequency characteristics.•The efficiency was validated by simulated fault bearings and real fault bearings.

论文关键词：Weak fault feature extraction,Intelligent fault diagnosis,Rolling bearings,Temporal feature,Noise conditions

论文评审过程：Received 12 May 2020, Revised 14 April 2021, Accepted 18 August 2021, Available online 20 August 2021, Version of Record 31 August 2021.

论文官网地址：https://doi.org/10.1016/j.knosys.2021.107413