A Kolmogorov–Arnold-Informed Interpretable Graph Wavelet Activation Network for Machine Fault Diagnosis

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…

• NN
  National Natural Science Foundation of China

  Awards: 92360308, 52505107