Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples

作者：

Highlights：

• Multi-wavelet is used to design new-type deep auto-encoder.

• Similarity measure is used to select high-quality auxiliary samples from source domain.

• Parameter knowledge is transferred using very few target training samples.

• Transfer diagnosis cases of fault severities and compound faults are used for verification.

摘要

•Multi-wavelet is used to design new-type deep auto-encoder.•Similarity measure is used to select high-quality auxiliary samples from source domain.•Parameter knowledge is transferred using very few target training samples.•Transfer diagnosis cases of fault severities and compound faults are used for verification.

论文关键词：Deep transfer multi-wavelet auto-encode,Gearbox fault,Transfer diagnosis,Variable working conditions,Few target training samples

论文评审过程：Received 25 September 2019, Revised 1 November 2019, Accepted 28 November 2019, Available online 30 November 2019, Version of Record 8 February 2020.

论文官网地址：https://doi.org/10.1016/j.knosys.2019.105313