High-Reliable Classification of Multiple Induction Motor Faults Using Vibration Signatures based on an EM Algorithm

EM 알고리즘 기반 강인한 진동 특징을 이용한 고 신뢰성 유도 전동기 다중 결함 분류

Industrial processes need to be monitored in real-time based on the input-output data observed during their operation. Abnormalities in an induction motor should be detected early in order to avoid costly breakdowns. To early identify induction motor faults, this paper effectively estimates spectral envelopes of each induction motor fault by utilizing a linear prediction coding (LPC) analysis technique and an expectation maximization (EM) algorithm. Moreover, this paper classifies induction motor faults into their corresponding categories by calculating Mahalanobis distance using the estimated spectral envelopes and finding the minimum distance. Experimental results shows that the proposed approach yields higher classification accuracies than the state-of-the-art approach for both noiseless and noisy environments for identifying the induction motor faults.