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

Pages : 813-823

Doi:10.3906/elk-1210-58

View : 17 | Download : 9

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :This paper focuses on detecting the static eccentricity and bearing faults of a permanent magnet synchronous motor insert ignore into journalissuearticles values(PMSM); using probability distributions based on equal width discretization insert ignore into journalissuearticles values(EWD); and a multilayer perceptron neural network insert ignore into journalissuearticles values(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