A Kolmogorov–Arnold-Informed Interpretable Graph Wavelet Activation Network for Machine Fault Diagnosis
Tianfu Li, Chuang Sun, Zhibin Zhao, Tao Liu, Xuefeng Chen, Ruqiang Yan
Abstract
The intelligent fault diagnosis (IFD) methods based on graph neural networks (GNNs) have achieved great success in machine fault diagnosis. However, the following two drawbacks of the existing GNN-based methods have greatly limited their application in industry: 1) poor interpretability in model structure and the extracted features and 2) difficulty in extracting robust fault features in nonstationary machine states. To address the above issues, a Kolmogorov–Arnold-informed interpretable graph wavelet activation network (GWAN) is proposed for machine fault diagnosis in this work. In GWAN, two critical components are designed, that is, graph wavelet activation convolutional (GWAConv) layer and wavelet attention (WavAtt) layer. In GWAConv, the graph message passing is achieved using the wavelet Kolmogorov–Arnold (WKA) layer with learnable scale and translation parameters to capture the robust fault features, while WavAtt layer decomposes the raw signal into low-frequency and high-frequency components to force the model to focus on the low-frequency components, which is helpful for fault diagnosis. Experiments under stationary, nonstationary, and noisy conditions were implemented to verify the effectiveness of GWAN. The experimental results show the superiority of GWAN among comparison methods, and the interpretability of the extracted features is demonstrated through post-hoc feature visualization. The code library is available at: https://github.com/HazeDT/GWAN