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

Random vector functional link neural network (RVFLNN) is an effective and powerful neural network model, and it has been commonly used for various engineering applications. In this paper, an improved RVFLNN model called discriminative manifold RVFLNN (DMRVFLNN) is proposed and applied for rolling bearing fault diagnosis, which has the following outstanding characteristics. First, DMRVFLNN relaxes the strict one-hot label matrix into a soft label matrix to fully exploit the interclass discriminative information and simultaneously enlarge the distances between interclass samples. Second, a within-class similarity graph based on manifold learning is designed for DMRVFLNN to force interclass samples to keep close together as far as possible. In the construction process of the within-class similarity graph, cosine distance is used to measure the distances between samples to enhance its robustness. Moreover, we design an effective update method solution for DMRVFLNN with a closed-form solution in each iteration. Two rolling bearing datasets are used to verify the fault diagnosis performance, and the experimental results prove the effectiveness and superiority of the proposed method for rolling bearing fault diagnosis. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The paper proposes an improved neural network model DMRVFLNN, which achieves good results in rolling bearing fault diagnosis. DMRVFLNN enhances fault diagnosis performance through methods such as soft label matrix and within-class similarity graph.