Diagnosis of Stator Winding and Permanent Magnet Faults of PMSM Drive Using Shallow Neural Networks

Abstract

This paper presents the application of shallow neural networks (SNNs): multi-layer perceptron (MLP) and self-organizing Kohonen maps (SOMs) to the early detection and classification of the stator and rotor faults in permanent magnet synchronous motors (PMSMs). The neural networks were trained based on the vector coming from measurements on a real object. The elements of the input vector of SNNs constituted the selected amplitudes of the diagnostic signal spectrum. The stator current and axial flux were used as diagnostic signals. The test object was a 2.5 kW PMSM motor supplied by a frequency converter operating in a closed-loop control structure. The experimental verification of the proposed diagnostic system was carried out for variable load conditions and values of the supply voltage frequency. The obtained results were compared with an approach based on a deep neural network (DNN). The research presented in the article confirm the possibility of detection and assessing the individual damage of stator winding and permanent magnets as well as the simultaneous faults of the PMSM stator and rotor using SNNs with simple signal preprocessing.