DGTL-Net: A Deep Generative Transfer Learning Network for Fault Diagnostics on New Hard Disks

作者：

Highlights：

• Generalization of diagnosis model is improved on new hard disks without faults.

• New faulty samples of hard disks could be generated from healthy samples.

• Distribution discrepancy between different types of hard disks could be decreased.

• End-end EM-based training strategy guarantees a good accuracy and convergence.

摘要

•Generalization of diagnosis model is improved on new hard disks without faults.•New faulty samples of hard disks could be generated from healthy samples.•Distribution discrepancy between different types of hard disks could be decreased.•End-end EM-based training strategy guarantees a good accuracy and convergence.

论文关键词：AIOps,Industrial applications,Hard disks,Fault diagnostics,Deep generative network,Deep transfer network

论文评审过程：Received 11 September 2020, Revised 10 November 2020, Accepted 24 November 2020, Available online 8 December 2020, Version of Record 25 December 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.114379