Advanced thermal vision techniques for enhanced fault diagnosis in electrical equipment: a review

A. Sasithradevi, J. Persiya, S. Mohamed Mansoor Roomi, D. Arumuga Perumal (), P. Prakash, M. Vijayalakshmi and L. Brighty Ebenezer
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A. Sasithradevi: Vellore Institute of Technology
J. Persiya: Vellore Institute of Technology
S. Mohamed Mansoor Roomi: Thiagarajar College of Engineering
D. Arumuga Perumal: National Institute of Technology Karnataka
P. Prakash: Anna University, MIT Campus
M. Vijayalakshmi: Vellore Institute of Technology
L. Brighty Ebenezer: Vellore Institute of Technology

International Journal of System Assurance Engineering and Management, 2025, vol. 16, issue 5, No 16, 1914-1932

Abstract: Abstract 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.

Keywords: Fault diagnosis; Infrared thermography; Electrical equipment; Machine learning; Deep learning; Segmentation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13198-025-02782-9

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