Active learning for new-fault class sample recovery in electrical submersible pump fault diagnosis

作者：

Highlights：

• Active learning to enable class sample recovery of electrical submersible pump.

• Active learning increases performance even with a restricted number of samples.

• The proposed acquisition strategy better acquires samples of a certain class.

• Proposed acquisition strategy reduces human effort on data for deep leaning models.

摘要

•Active learning to enable class sample recovery of electrical submersible pump.•Active learning increases performance even with a restricted number of samples.•The proposed acquisition strategy better acquires samples of a certain class.•Proposed acquisition strategy reduces human effort on data for deep leaning models.

论文关键词：Fault diagnosis,Electrical submersible pump,Classification,Active learning,Deep learning

论文评审过程：Received 23 May 2022, Revised 29 July 2022, Accepted 8 August 2022, Available online 13 August 2022, Version of Record 6 September 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118508