Data alignments in machinery remaining useful life prediction using deep adversarial neural networks

作者：

Highlights：

• A deep learning-based prognostic method is proposed for rotating machines.

• Generative adversarial networks formulate a health indicator for degradation.

• Data distribution discrepancy is minimized through adversarial training.

• The proposed scheme is validated through experiments on two datasets.

摘要

•A deep learning-based prognostic method is proposed for rotating machines.•Generative adversarial networks formulate a health indicator for degradation.•Data distribution discrepancy is minimized through adversarial training.•The proposed scheme is validated through experiments on two datasets.

论文关键词：Remaining useful life prediction,Rotating machines,Deep learning,Adversarial training,Data alignment

论文评审过程：Received 26 September 2019, Revised 25 January 2020, Accepted 29 March 2020, Available online 16 April 2020, Version of Record 24 April 2020.

论文官网地址：https://doi.org/10.1016/j.knosys.2020.105843