Research on Intelligent Fault Diagnosis of Rolling Bearing Based on Improved ShufflenetV2-LSTM

Abstract

The timing characteristics of the fault vibration signal of the rolling bearing are ignored by the ShufflenetV2 network. For the bearing fault problem of multiple working conditions, the fault diagnosis signal is extracted by the feature, which cannot be performed efficiently and accurately. The ShufflenetV2 network has a deep number of layers and a large amount of parameters, which causes the network to be prone to overfitting. In response to the above problems, an improved ShufflenetV2-LSTM intelligent fault diagnosis system is proposed, which is a model in which the long short-term memory network (LSTM) layer and the Dropout layer are serially embedded in the ShufflenetV2 network. This method preserves the ability of the ShufflenetV2 network to extract features, and the advantage of the ability of LSTM to strengthen the data sequence is also inherited, so this improves the accuracy of fault diagnosis. The generalization ability of the model can be enhanced by Dropout, which can effectively suppress the degree of overfitting. In addition, this paper also optimized the activation function and optimizer in the model to make up for the additional time cost brought by the Dropout layer, so that the robustness of the system is improved and fault diagnosis can be analyzed efficiently. Experimental analysis shows that the diagnosis accuracy of the proposed algorithm is as high as 97% and early failures of rolling bearings can be effectively identified in real time.