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

被引：0
作者：
A. Sasithradevi [1 ]
J. Persiya [2 ]
S. Mohamed Mansoor Roomi [3 ]
D. Arumuga Perumal [4 ]
P. Prakash [5 ]
M. Vijayalakshmi [2 ]
L. Brighty Ebenezer [2 ]
机构：
[1] Centre for Advanced Data Science, Vellore Institute of Technology, Tamil Nadu, Chennai
[2] School of Electronics Engineering, Vellore Institute of Technology, Tamil Nadu, Chennai
[3] Department of Electronics and Communication Engineering, Thiagarajar College of Engineering, Tamilnadu, Madurai
[4] Department of Mechanical Engineering, National Institute of Technology Karnataka, Mangalore, Surathkal
[5] Department of Electronics Engineering, Anna University, MIT Campus, Chennai
来源：
International Journal of System Assurance Engineering and Management | 2025年 / 16卷 / 5期
关键词：
Deep learning; Electrical equipment; Fault diagnosis; Infrared thermography; Machine learning; Segmentation;
D O I：
10.1007/s13198-025-02782-9
中图分类号：
学科分类号：
摘要：
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.
引用
收藏
页码：1914 / 1932
页数：18
相关论文
共 103 条
[1]  
Abdel-Qader I., Yohali S., Abudayyeh O., Yehia S., Segmentation of thermal images for non-destructive evaluation of bridge decks, NDT E Int, 41, 5, pp. 395-405, (2008)
[2]  
Akbar S., Vaimann T., Asad B., Kallaste A., Sardar M.U., Kudelina K., State-of-the-art techniques for fault diagnosis in electrical machines: advancements and future directions, Energies, 16, 17, (2023)
[3]  
Al Thobiani F., Tran V.T., Tinga T., An Approach to fault diagnosis of rotating machinery using the second-order statistical features of thermal images and simplified fuzzy ARTMAP, Engineering, 9, 6, pp. 524-539, (2017)
[4]  
Al-Musawi A.K., Anayi F., Packianather M., Three-phase induction motor fault detection based on thermal image segmentation, Infrared Phys Technol, 104, (2020)
[5]  
Al-Obaidy F., Yazdani F., Mohammadi F.A., Fault detection using thermal image based on soft computing methods: comparative study, Microelectron Reliab, 71, pp. 56-64, (2017)
[6]  
Alwar S., Samithas D., Boominathan M.S., Balachandran P.K., Mihet-Popa L., Performance analysis of thermal image processing-based photovoltaic fault detection and PV array reconfiguration—a detailed experimentation, Energies, (2022)
[7]  
Antonino-Daviu J.A., Strangas E.G., Fault diagnosis, prognosis, and reliability of electric motors and drives: open questions, challenges and perspectives, 2022 international conference on electrical machines (ICEM), pp. 731-737, (2022)
[8]  
Asgari S., Gupta R., Puri I.K., Zheng R., A data-driven approach to simultaneous fault detection and diagnosis in data centers, Appl Soft Comput, 110, (2021)
[9]  
Avina-Corral V., de Jesus Rangel-Magdaleno J., Barron-Zambrano J.H., Rosales-Nunez S., Review of fault detection techniques in power converters: fault analysis and diagnostic methodologies, Measurement, 234, (2024)
[10]  
Bagavathiappan S., Lahiri B.B., Saravanan T., Philip J., Jayakumar T., Infrared thermography for condition monitoring - a review, Infrared Phys Technol, 60, pp. 35-55, (2013)
12345678910