Domain-generalized multi-channel fusion for bearing fault diagnosis under variable operating conditions
To address the issues of low accuracy and insufficient generalization capabilities in traditional methods for diagnosing bearing faults under variable operating conditions, we propose a vision-temporal bimodal multi-channel feature fusion method for rolling bearing fault diagnosis based on domain generalization (DG). This approach constructs a parallel architecture for extracting bimodal features: on one hand, multiple signal processing techniques are employed to transform raw vibration signals into multi-perspective two-dimensional visual feature maps as visual modality input, while simultaneously employing variational modal decomposition to decompose vibration signals into a series of eigenmode functions constituting the temporal modality input. At the model level, a multi-channel large-kernel convolutional network and a global attention-enhanced bidirectional gated recurrent unit network are designed to extract deep features from the visual and temporal modalities, respectively. Subsequently, feature vectors from each channel are concatenated in the feature dimension, with fault classification performed via a progressive dimensionality reduction classifier. Experiments conducted using bearing datasets from case western reserve university and the University of Paderborn in Germany demonstrate that this method can diagnose bearing failures under cross-conditions—even when trained solely on source-domain data and without exposure to target-domain data during training—and that its DG accuracy outperforms that of existing mainstream advanced methods.