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Intelligent fault diagnosis of three-phase asynchronous motor based on PCA-SVCNN

In order to solve the problems caused by the complex motor structure signals and big data of non-stationary machinery in the traditional asynchronous motor fault diagnosis method, the speed and accuracy of three-phase asynchronous motor fault diagnosis are improved. In this paper, a new fault diagnosis method of three-phase asynchronous motor is proposed. Firstly, the principal component analysis (PCA) method is used to reduce the dimension of the collected current data, and then the support vector machine (SVM) is used to realise the two classification of the data. Finally, the two types of data are classified by convolutional neural network (CNN), and the accurate diagnosis of three-phase asynchronous motor fault can be realised. The simulation results show that the proposed algorithm can improve the accuracy of fault classification quickly and effectively, which is of great significance to the accurate diagnosis of motor faults.