Discriminative manifold random vector functional link neural network for rolling bearing fault diagnosis

作者：

Highlights：

• A novel DMRVFLNN model is proposed for rolling bearing fault diagnosis.

• DMRVFLNN uses a soft label matrix to enlarge the distances of interclass samples.

• DMRVFLNN uses a manifold graph to enhance the compactness of within-class samples.

• We devise an iterative update method to solve the DMRVFLNN model.

• Two fault datasets of bearing are used to verify the superiority of DMRVFLNN.

摘要

•A novel DMRVFLNN model is proposed for rolling bearing fault diagnosis.•DMRVFLNN uses a soft label matrix to enlarge the distances of interclass samples.•DMRVFLNN uses a manifold graph to enhance the compactness of within-class samples.•We devise an iterative update method to solve the DMRVFLNN model.•Two fault datasets of bearing are used to verify the superiority of DMRVFLNN.

论文关键词：Fault diagnosis,Discriminative manifold random vector functional link neural network,Soft label matrix,Within-class similarity graph,Rolling bearing

论文评审过程：Received 29 June 2020, Revised 15 September 2020, Accepted 6 October 2020, Available online 22 October 2020, Version of Record 27 October 2020.

论文官网地址：https://doi.org/10.1016/j.knosys.2020.106507