A robust method for detection and classification of permanent magnet synchronous motor faults: Deep autoencoders and data fusion approach

Abstract

Permanent magnet synchronous motors become popular in wind turbines and industrial applications. In critical machines, it is necessary to use robust condition monitoring and fault diagnosis algorithms to prevent faults or shutdowns. The data-driven approach with machine learning algorithms is widely used in industrial and research communities as this method does not require a mathematical model of the system, which is difficult to obtain in practical cases. Most of the successful machine learning methods are based on supervised learning approach, requiring labelled training data. The supervised learning approach cannot use the unlabelled data, while only a few labelled data is in place in the industry. This work uses a deep autoencoder based unsupervised learning method to identify the features of the fault classification algorithm in a self-supervised way, which overcome the shortage of labelled data. The proposed algorithm uses the benefits of available unlabelled data, but it needs only a few labelled data. The fault classification algorithm is based on artificial neural network SoftMax layer and Bayes classifier. The robustness of the algorithm is improved by fusing the current and vibration information. Experimental results are used to validate the robustness of proposed algorithms under noise conditions, and the results show that the algorithm could classify faults robustly.