A Rolling Bearing Fault Diagnosis Method Combining MSSSA-VMD with the Parallel Network of GASF-CNN and BiLSTM

Once the rolling bearing fails, it will threaten the normal operation of the whole rotating machinery. Therefore, it is very necessary to conduct research on rolling bearing fault diagnosis. This paper proposes a rolling bearing fault diagnosis method combining MSSSA-VMD (variational mode decomposition optimized by the improved salp swarm algorithm based on mixed strategy) with the parallel network of GASF-CNN (convolutional neural network based on Gramian angular summation field) and bi-directional long short-term memory (BiLSTM) to solve the problem of poor diagnostic performance for the rolling bearing faults caused by the respective limitations of existing fault diagnosis methods based on signal processing and deep learning. Firstly, MSSSA-VMD is proposed to solve the problem where the decomposition effect of VMD is not ideal due to improper parameter selection. Then, MSSSA-VMD is employed to preprocess and extract characteristics. Finally, the extracted characteristics are input into the parallel network of GASF-CNN and BiLSTM for diagnosis. In one channel of the parallel network, GASF is used to convert the characteristic vectors into a two-dimensional image, which is then fed into CNN for spatial characteristic extraction. In the other channel of the parallel network, the characteristic vectors are directly input into BiLSTM for temporal characteristic extraction. Experimental results demonstrate that the proposed method has good performance in terms of fault diagnosis performance under constant operating conditions, generalization ability under variable operating conditions and noise resistance.