Naïve Bayes Classification Technique for Brushless DC Motor Fault Diagnosis with Discrete Wavelet Transform Feature Extraction

Abstract

Mechanical faults often occur in BLDC motors. These machines are essential to each respective industry. When a fault is not detected, it will cause the machine to stop functioning to its intended purpose. A mechanical fault diagnostic system using Naïve Bayes classifier with the DWT feature extraction method was proposed in this study. A single-level DWT was used to extract and decompose the recorded motor voltage signals, split into 7030, 70% for the training set, and 30% for the testing set. The accuracy in training the Naïve Bayes classifier is 97.2%. Using the trained model to the remaining test set resulted in an accuracy of 87.3% for detecting mechanical motor faults in a BLDC motor.