Classification of Optoelectronic Rotary Encoder Faults Based on Deep Learning Methods in Permanent Magnet Synchronous Motor Drive System

Abstract

This article presents the classification of optoelectronics encoder faults in a permanent magnet synchronous motor (PMSM) drive system. This paper proposes the deep neural networks (DNNs) speed sensor faults classification application in the vector-controlled PMSM drive. This approach to the issue has not been discussed in the literature before. This work presents a solution based on early detection with the use of the model reference adaptive system (MRAS) estimator and fault classification based on artificial intelligence. The innovative nature of this work is also due to the simulation of speed sensor damage using the developed optoelectronics encoder model in the Matlab/Simulink environment. This work is focused on simulation studies, which have been supported by experimental results obtained on the MicroLabBox platform. This article compares two structures of deep neural networks in fault detection. The results were also compared with previous experimental studies on the classification of speed sensor failures using shallow neural networks.