Fault diagnosis of rotating machinery based on graph weighted reinforcement networks under small samples and strong noise

Available fault vibration signals of large rotating machines are usually limited and consist of strong noise. Existing deep learning methods do not sufficiently extract the correlation relationship between samples, and the process of extracting features is easily disturbed by noise. Therefore, the accuracy of rotating machinery fault diagnosis needs further improvement. A graph-weighted reinforcement network (GWRNet) is proposed to accurately diagnose the faults of rotating machines under small samples and strong noise. First, an adjacency matrix was constructed by measuring the Euclidean distance of the time- and frequency-domain characteristics of small samples to achieve the pre-classification of nodes. Second, the node feature aggregation strategy was designed by dynamically enhancing the largest weight of the multiheaded attention matrix to suppress strong noise interference. Finally, the effectiveness of the proposed method was verified using datasets from the drivetrain diagnostics simulator (DDS) test rig and wind turbine gearboxes.

Automated summary

This paper proposes a method using graph-weighted reinforcement network to accurately diagnose faults in rotating machinery. The method addresses the issues of limited samples and noise interference by constructing an adjacency matrix and designing a node feature aggregation strategy.