A study on adaptation lightweight architecture based deep learning models for bearing fault diagnosis under varying working conditions

作者：

Highlights：

• Bearing fault diagnosis under varying working conditions.

• Improving the quality of spectrums via considering practical distribution.

• Lightweight models implementation in industrial diagnostic scenarios.

• Realizing transfer learning with feature adaptation in unsupervised tasks.

摘要

•Bearing fault diagnosis under varying working conditions.•Improving the quality of spectrums via considering practical distribution.•Lightweight models implementation in industrial diagnostic scenarios.•Realizing transfer learning with feature adaptation in unsupervised tasks.

论文关键词：Diagnosis,CNN,Normalization,Lightweight,Transfer learning

论文评审过程：Received 2 February 2020, Revised 10 June 2020, Accepted 29 June 2020, Available online 8 July 2020, Version of Record 28 July 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113710