Bearing fault diagnosis method based on multi-branch convolutional neural network

Traditional rolling bearing intelligent fault diagnosis methods using a single signal input often face challenges such as low diagnostic accuracy and poor robustness to noise in practical applications. This paper proposes a multi-branch convolutional neural network (CNN) model based on multi-scale and multi-modal input data. The model realizes multimodal multiscale inputs by means of variational modal decomposition (VMD) and STFT transforms while combining the original signals. Compared with a single signal input, richer and complementary fault features can be extracted, fully reflecting the multidimensional information of the fault. By designing multiple parallel CNN branches, the model is able to extract key features from the signal at different scales and modalities and perform feature fusion through the attention mechanism, which further improves the diagnostic accuracy and robustness to noise.