Dynamic Graph-Based Feature Learning With Few Edges Considering Noisy Samples for Rotating Machinery Fault Diagnosis

Due to its ability to learn the relationship among nodes from graph data, the graph convolution network (GCN) has received extensive attention. In the machine fault diagnosis field, it needs to construct input graphs reflecting features and relationships of the monitoring signals. Thus, the quality of the input graph affects the diagnostic performance. But it still has two limitations: 1) the constructed input graph usually has redundant edges, consuming excessive computational costs; 2) the constructed input graph cannot reflect the relationship between the noisy signals well. In order to overcome them, a dynamic graph-based feature learning with few edges considering noisy samples is proposed for rotating machinery fault diagnosis in this article. Noisy vibration signals are converted into one spectrum feature-based static graph, where redundant edges are simplified by the distance metric function. Edge connections of the input static graph are updated according to the relationship among high-level features extracted by the GCN. Based on this, dynamic input graphs are reconstructed as new graph representations for noisy samples. To verify the effectiveness of the proposed method, validation experiments were conducted on practical platforms, and results show that the dynamic input graph with few edges can effectively improve the diagnostic performance under different SNRs.

Automated summary

This article proposes a dynamic graph-based feature learning method for rotating machinery fault diagnosis. Noisy vibration signals are converted into static graphs, where redundant edges are simplified and edge connections are updated based on the relationship among high-level features. The dynamic input graph, constructed as a new graph representation for noisy samples, effectively improves diagnostic performance under different signal-to-noise ratios.