Publication: Fault detection of electrical motor based on thermal imaging and machine learning
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Date
2023-08
Authors
Yap, Jun Hong
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Abstract
Faults could occur on electrical motor due to various reasons, and an early motor fault detection system helps prevent interruption in service and financial losses. However, the current practice of manual fault inspection and preventive maintenance is time consuming, and it may not be effective. Thus, motor fault diagnosis using thermal imaging technique has been on the rise in recent years. To further improve the effectiveness and to automate fault detection using thermal imaging, artificial intelligence (AI) can be employed. Hence, in this project, an electrical motor fault detection system based on thermal imaging and machine learning (ML) technique was developed. Transfer learning (TL) approach using pre-trained convolutional neural networks (CNNs) was used. The CNN was trained to learn the features extracted from the thermal images of a faulty and a healthy motor and use them to diagnose the condition of the motor. Various
hyperparameters were configured for network training to obtain the best results. Furthermore, performance analysis was conducted and discussed to evaluate the credibility and reliability of the trained network. A Graphical User Interface (GUI) was then created to ease the user in using the proposed fault detection system by just supplying the thermal images of a test motor to the GUI application for fault diagnosis. The evaluation results showed that GoogLeNet gives the best detection performance with both the mini-batch and the validation accuracy achieving a 100%, and both the losses were low as well, at 0.0015 and 0.0001 respectively. Thus, the final trained network based on GoogLeNet was used in the GUI for the implementation of the proposed motor fault detection system. In conclusion, the aim for implementing a fault detection system and GUI, through the use of thermal images and machine learning was achieved.