Turn‐to‐turn short circuit of motor stator fault diagnosis in continuous state based on deep auto‐encoder

Abstract

In this study, a turn-to-turn short circuit of motor stator fault diagnosis system based on deep auto-encoder and soft-max classifier is proposed. It is also considered in the proposed system that an unbalanced power supply has similar current characteristics and is possible to be misidentified. The determination of neural network parameters and their effect in training are given systematically, which improves the performance compared with traditional auto-encoder. The proposed fault diagnosis system can map the preprocessed three-phase motor current signals to a two-dimensional vector, corresponding to certain area of the feature plane to identify different fault types. The proposed system is verified via simulation experiment using MATLAB and physical experiment using a motor in laboratory. The conclusion illustrates that when signals originating from discrete motor speed, load, and fault severity are used in training, the fault diagnosis is still effective in continuous state. The accuracy is 89% for speed verification and 95% for load verification in simulation and is above 99% in the physical experiment.