AI-Driven Thermography-based Fault Diagnosis in Single-Phase Induction Motor

Abstract

Single-phase induction motors (SIMs) are commonly used in industrial applications. The extensive industrial usage of SIMs requires proper maintenance and fault detection. Among various faults, the most common mechanical faults in SIMs are bearing faults. Thus, detecting these faults during motor operation is essential for preventing damage. Many fault diagnosis methods have been proposed to detect bearing faults based on contact sensors, but they have accessibility problems. This paper presents a novel Infrared Thermography (IRT) based fault diagnosis method, leveraging both conventional machine learning (CML) and deep learning (DL) techniques for motor condition classification. In CML, Statistical and Gray Level Co-occurrence Matrix (GLCM) features are extracted from thermal images. The support vector machine recursive feature elimination (SVM-RFE) method is used to select the most relevant high-score features from the extracted features. Support vector machine (SVM) with linear and radial basis function (RBF) kernel functions and K-nearest neighbours (KNN) classifiers are applied to the selected interpretable features to classify machines into five classes of healthy, inner race, outer race, missing ball with low lubrication, and no lubrication bearing faults. Furthermore, in DL, a convolutional neural network (CNN) of few simple convolutional layers with several filters is also used as a healthy and faulty bearings classifier. The proposed method achieves classification accuracy of 98.29% using CML and 100% using DL. Moreover, our IRT-based SIMs bearing fault diagnosis methods demonstrate a reduction in architectural complexity compared to prevailing state-of-the-art approaches.