Interpretable semi-supervised wavelet graph convolutional network for noise-robust fault diagnosis of rotating machinery under varying operating conditions

Vibration signals of rotating machinery collected in industrial scenarios are usually corrupted by heavy noise, and time-varying operating conditions further degrade the performance of intelligent fault diagnosis methods. To address these issues, this article proposes an interpretable semi-supervised wavelet graph convolutional network (SW-GCN) for noise-robust fault diagnosis under varying operating conditions. First, a multi-wavelet graph convolution kernel with a scale-smoothing property is designed, and an interpretable wavelet graph convolution layer is constructed to improve denoising capability and model interpretability. Second, an entropy-weighted semi-supervised network architecture is developed to excavate node correlation within graph data and enhance the capability of capturing fault-sensitive features under variable working conditions. Finally, feature extraction and denoising mechanisms are visualized to interpret the internal logic of SW-GCN and increase the trustworthiness of diagnosis results. Two rotating machinery datasets under different noise intensities and time-varying speeds are utilized for validation. The results demonstrate that the proposed SW-GCN can effectively extract fault-sensitive features under strong noise interference, achieving competitive diagnostic accuracy and prominent noise robustness.