Faculty Publications
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Item Advanced thermal vision techniques for enhanced fault diagnosis in electrical equipment: a review(Springer, 2025) Anbalagan, A.; Persiya, J.; Mohamed Mansoor Roomi, S.; Arumuga Perumal, D.A.; Poornachari, P.; Vijayalakshmi, M.; Ebenezer, L.Ensuring the reliability and safety of electrical equipment is essential for industrial and residential applications. Traditional fault diagnosis methods involving physical inspections are time-consuming and ineffective for early fault detection. Infrared (IR) thermography offers a non-invasive and efficient solution by identifying anomalies in temperature profiles. This review explores thermal vision-based fault diagnosis techniques, including region of interest (ROI) segmentation, image pre-processing, and fault diagnosis algorithms, with a focus on deep learning approaches. The study highlights the effectiveness of machine learning models in enhancing fault detection accuracy while identifying challenges such as environmental variations, data inconsistencies, and system integration issues. The review discusses the role of real-time applications, wireless technologies, and AI-based automation in improving fault detection. Research gaps are identified, and future directions are proposed to enhance efficiency, reliability, and industrial adoption. © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2025.Item Infrared Perspectives: Computing laptop energy dissipation via thermal imaging and the Stefan-Boltzmann equation(Elsevier Ltd, 2024) Anbalagan, A.; Arumuga Perumal, D.; Persiya, J.Energy conservation is crucial for reducing greenhouse gas emissions and addressing climate change. Laptops contribute significantly to energy consumption, emphasizing the need for improved energy efficiency. This paper explores the application of thermal imaging technology to enhance energy conservation in laptops. Thermal imaging provides valuable insights into heat distribution on the laptop's surface, aiding in identifying areas of excessive energy consumption. By identifying areas of a laptop that generate excessive heat and implementing energy-efficient measures, energy consumption can be reduced, and the device's lifespan can be extended. The study leverages computer vision and artificial intelligence techniques to analyze thermal images. We collected the thermal images for the dataset using the FLIR E75 Thermal camera. Two methods of Region of Interest (ROI) extraction, contour-based thresholding, and Detectron2-based extraction are employed. Feature extraction includes statistical, texture, spatial, and energy features, and Principal Component Analysis (PCA) is used to reduce dimensionality. K-means clustering categorizes data points based on reduced features, and performance metrics validate the effectiveness of the clustering methods. The study also computes energy dissipation from thermal images using the Stefan-Boltzmann Law. Results indicate that thermal imaging, coupled with advanced analysis techniques, holds promise for improving energy conservation in laptops. © 2024 Elsevier Ltd