Research on Rolling Bearing On-Line Fault Diagnosis Based on Multi-dimensional Feature Extraction

In the paper, a novel rolling bearing fault diagnostic method was proposed to fulfill the requirements for effective assessment of different fault types and severities with real-time computational performance. Firstly, multi-dimensional feature extraction is discussed. And secondly, a gray relation algorithm was used to acquire basic belief assignments. Finally, the basic belief assignments were fused through Yager algorithm. The related experimental study has illustrated the proposed method can effectively and efficiently recognize various fault types and severities.