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

Authors

MEHMET AKAR
MAHMUT HEKİM
UMUT ORHAN

DOI

10.3906/elk-1210-58

Abstract

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.

Keywords

Permanent magnet synchronous motor (PMSM), eccentricity, bearing faults, equal width discretization (EWD), probability distribution, artificial neural network

First Page

813

Last Page

823

Recommended Citation

AKAR, MEHMET; HEKİM, MAHMUT; and ORHAN, UMUT (2015) "Mechanical fault detection in permanent magnet synchronous motors using equal width discretization-based probability distribution and a neural network model," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 23: No. 3, Article 14. https://doi.org/10.3906/elk-1210-58
Available at: https://journals.tubitak.gov.tr/elektrik/vol23/iss3/14