Euclidean distance based feature ranking and subset selection for bearing fault diagnosis

作者：

Highlights：

• A novel feature ranking technique for bearing fault diagnosis is proposed.

• Several machine learning and artificial intelligence classifiers are used for validation.

• Fewer feature subset is required with proposed methodology.

• It has higher classification accuracy with less time consumption.

摘要

•A novel feature ranking technique for bearing fault diagnosis is proposed.•Several machine learning and artificial intelligence classifiers are used for validation.•Fewer feature subset is required with proposed methodology.•It has higher classification accuracy with less time consumption.

论文关键词：Bearing,Euclidean distance,Defect,Envelope analysis,Fault diagnosis,Feature ranking

论文评审过程：Received 19 December 2019, Revised 15 March 2020, Accepted 20 March 2020, Available online 9 April 2020, Version of Record 27 April 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113400