Brushless DC Motor Fault Classification Using Support Vector Machine Algorithm with Discrete Wavelet Transform Feature Extraction

A motor fault is a common problem in BLDC motors. If the fault is not detected, it can cause malfunctions in the machines. A mechanical motor fault diagnosis using a Support Vector Machine with Discrete Wavelet Transform feature extraction is initiated in this research. It will be compared to the previous study of Borja et al., which utilizes the K-nearest neighbor alongside the Discrete Wavelet Transform in diagnosing motor faults. Paired T-test was used to differentiate the capabilities of SVM-DWT and K-NN-DWT. It shows the accuracy of the two classifiers; the Support Vector Machine gets an accuracy of 98.67%, while K-Nearest Neighbor obtains an accuracy of 97.49%. The results prove that the Support Vector Machine was more capable than K-Nearest Neighbor in diagnosing motor faults.