Induction Motor Fault Detection and Classification using RCNN and SURF Based Machine Learning Algorithms and Infrared Thermography

Sasikumar, B.; Venkatasalam, K.; Rajendran, P.

doi:10.24200/sci.2024.61897.7589

Induction Motor Fault Detection and Classification using RCNN and SURF Based Machine Learning Algorithms and Infrared Thermography

Document Type : Article

Authors

¹ Department of Artificial Intelligence and Data Science, Knowledge Institute of Technology, Salem, Tamil Nadu, India

² Department of Computer Science and Engineering, Mahendra Engineering College, Namakkal, Tamil Nadu, India

³ Department of Computer Science and Engineering, Knowledge Institute of Technology, Salem, Tamil Nadu, India

10.24200/sci.2024.61897.7589

Abstract

Induction motors in electrical industries face stress and potential faults. Preventive maintenance, including fault detection, is vital for safety and energy conservation. Infrared imaging, though underutilized, can monitor machine conditions effectively. In response to this gap, this paper presents a novel motor fault identification method employing infrared thermography (IRT) in combination with image processing and machine learning techniques, with a particular focus on energy efficiency. IRT is harnessed for early fault detection to promote energy conservation. The approach involves the extraction of color and texture features from the motor's infrared images using the Gabor filter and GNS (global neighborhood structure) map. The proposed method integrates the faster R-CNN (Region-based Convolutional Neural Network) with the Speeded Up Robust Features (SURF) algorithm to enhance fault detection and classification accuracy. SURF serves as a feature descriptor for faster R-CNN, enabling object detection and fault classification based on the extracted features. Additionally, efficiency is assessed using the Finite Element Method (FEM) based on stator and rotor power, contributing to energy conservation through early fault detection in motors. Notably, the proposed motor fault classification is applicable under various loading conditions, consistently achieving accuracy rates exceeding 90%.

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