Domain Generalization Fault Diagnosis Method Based on Multi-Level Feature Alignment for Wind Turbine Planetary Gearbox

Existing deep learning-based fault diagnosis models for wind turbine planetary gearboxes cannot cope with the problem of poor domain generalization ability caused by differences in data distribution under different operating conditions. To address this limitation, a domain generalization fault diagnosis method based on multi-level feature alignment (DGFD-MLFA) for planetary gearbox is proposed. Firstly, the multi-level feature extraction (MLFE) module is designed, which includes a convolutional denoising autoencoder, a multi-scale separable convolution, and a transformer encoder. The MLFE module can extract both local and global features while denoising. Secondly, the multi-level feature alignment (MLFA) module includes the Local Maximum Mean Discrepancy (LMMD) and the Maximum Mean Discrepancy (MMD) is designed to align local and global features respectively, allowing the model to ultimately extract domain-invariant features and enhancing its fault diagnosis capability under unknown operating conditions. Experiment results on nine domain generalization fault diagnosis tasks based on the wind turbine planetary gearbox dataset, including ablation studies and comparisons with different feature alignment methods, show that the proposed model significantly improves fault diagnosis accuracy under both interpolation and extrapolation domain generalization tasks.