A Multi-Fault Diagnosis Method for Cylindrical Roller Bearings Based on RSNGO-Optimized VMD and CNN-BiLSTM-SAT

To address the problems of severe feature coupling, difficult fault information extraction, and insufficient recognition accuracy for cylindrical roller bearings under multiple fault conditions, this paper proposes a multi-fault pattern recognition method based on a Northern Goshawk Optimization algorithm improved by refraction opposition-based learning and the sine–cosine algorithm (RSNGO). The RSNGO is used to optimize variational mode decomposition (VMD) and a convolutional neural network–bidirectional long short-term memory–self-attention (CNN–BiLSTM–SAT) network. First, RSNGO adaptively optimizes the number of decomposition modes and the penalty factor of VMD, and selects the optimal intrinsic mode function (IMF) components, from which time-domain statistical features are extracted to construct the sample set. Then, a CNN–BiLSTM–SAT diagnostic network is constructed, and RSNGO is employed to jointly optimize its key hyperparameters, including convolution kernel size, number of convolution kernels, number of BiLSTM hidden units, and initial learning rate. In this network, CNN extracts local features, BiLSTM models temporal dependencies, and the self-attention mechanism enhances the representation of critical fault features. Finally, the constructed feature samples are input into the optimized network to realize multi-fault pattern recognition of cylindrical roller bearings. Experimental results demonstrate that the proposed method effectively improves the separability and recognition accuracy of multi-fault features, exhibits strong robustness and generalization capability under complex operating conditions, and provides an effective solution for intelligent bearing fault diagnosis.