Electrical systems faults diagnosis based on thermography and machine learning techniques

Abstract

The goal of using AI-driven conditional monitoring in electrical devices is to monitor and trace the beginning and development of deterioration prior to a failure. This degradation eventually results in a system malfunction that impacts the availability of the whole system. Early identification allows for a planned shutdown, averting catastrophic failure and guaranteeing more cost-effective and dependable operation. This study is divided into two major parts: the first part deals with the identification and categorization of faults in induction motors, and the second part deals with the detection and classification of faults in transformers. In machine health management, condition monitoring and problem diagnostics of electrical machines are important study areas. Using infrared thermography method (IRT), a new noncontact and nonintrusive experimental framework is used in the first portion of this thesis to monitor and diagnose defects in a three-phase induction motor. Using IRT to obtain a thermograph of the target machine is the first step in the process. The Speeded-Up Robust Features (SURF) detector and descriptor are then used to extract fault features from the IRT images using the bag-of-visual-word (BoVW) technique. Then, a group learning method known as Extremely Randomized Tree (ERT) is applied to automatically detect different types of induction motor defect patterns. Based on experimental IRT images, the efficacy of the suggested method is evaluated, showcasing its potential as a potent diagnostic tool with superior classification accuracy and stability over alternative approaches. The second part of the thesis presents an experimental framework that uses infrared thermography (IRT) to monitor and diagnose transformer defects in a non-intrusive and non-contact manner. Using IRT to obtain a thermograph of the intended machine is the first step in the process. GIST features are then taken from the database's reference image and every other image. Finally, a machine learning technique known as Support Vector Machine (SVM) is used to automatically identify different fault patterns in the transformer. Based on experimental IRT images and diagnostic results, the efficacy and capacity of the proposed method are assessed, demonstrating its potential as a potent diagnostic tool with high classification accuracy and stability. This method improves operational reliability by facilitating the early identification and detection of transformer failures