Mechanical fault detection in permanent magnet synchronous motors using equal width discretization-based probability distribution and a neural network model

This paper focuses on detecting the static eccentricity and bearing faults of a permanent magnet synchronous motor (PMSM) using probability distributions based on equal width discretization (EWD) and a multilayer perceptron neural network (MLPNN) model. In order to achieve this, the PMSM stator current values were measured in the cases of healthy, static eccentricity, and bearing faults for the conditions of three speeds and five loads. The data was discretized into several ranges through the EWD method, the probability distributions were computed according to the number of current values belonging to each range, and these distributions were then used as inputs to the MLPNN model. We conducted eighteen experiments to evaluate the performance of the proposed model in the detection of faults. The proposed method was very successful in full load and high speed for some experiments. As a result, we showed that the proposed model resulted in a satisfactory classification of accuracy rates.