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Implementation of Permanent Magnet Synchronous Motor Fault Diagnosis by a Stacked Autoencoder

This manuscript presents an effective diagnosis algorithm for permanent magnet synchronous motors running with an array of faults of varying severity over a wide speed range. The fault diagnosis is based on a current signature analysis. The complete fault motor diagnosis system requires the extraction of data based on the proposed method, and a subsequent method for adding classifications. In this paper, we propose a feature extraction method using a stacked autoencoder and a classification method using a softmax layer. The results show that the proposed methods can effectively diagnose five different motor states, including two different demagnetization fault states and two bearing fault states.