JERAJournal of Electronic Research and Application2208-35022208-3510Bio-Byword Scientific Publishing Pty. Ltd.10.26689/jera.v10i4.14912ArticleResearch on the Application of Computer Vision in Equipment Fault Diagnosishttps://artdesignp.com/journal/JERA/10/4/10.26689/jera.v10i4.14912ZhuXiaoquan,WangSiyu,ZhangTong,ChenPengyuan20261042026-05-21With the continuous improvement of industrial automation, rapid and accurate diagnosis of equipment faults is the key to ensuring production safety and efficiency. With the advantages of non-contact sensing, real-time processing and high-precision recognition, computer vision has broad application prospects in fault diagnosis. This technology integrates image acquisition, feature extraction and deep learning models to automatically identify and classify equipment faults such as appearance damage, motion abnormalities and thermal state changes. Multi-modal image fusion further improves fault positioning accuracy under complex working conditions. In scenarios such as mine electrical equipment, construction engineering inspection cold-chain storage and unmanned aerial vehicle (UAV) inspection, its detection performance is superior to traditional methods, providing strong technical support for building an intelligent equipment operation and maintenance system and promoting the in-depth integration of industrial Internet and intelligent manufacturing.

Wang Q, 2026, Design of Intelligent Fault Diagnosis System for Electrical Equipment in Coal Mines based on Computer Vision and Deep Learning. International Journal of System Assurance Engineering and Management, 2026(prepublish): 1–11.

Lakhan M, Oskar G, A.P. W, et al., 2026, Detection of Glacier Calving Events from Time-Lapse Images Using Computer Vision and a Neural Network. Journal of Glaciology, 2026(72): e5.

Jiang T, Mu H, Liang L, et al., 2026, Robust Detection of Dead Broilers in Caged Environments Using Infrared-Visible Image Fusion and Computer Vision. Measurement, 258(PE): 119502–119502.

Feature Extraction and Image Processing for Computer Vision, Elsevier Ltd, October 11, 2025.

Harikrishna K, Sen B, Nithin A, et al., 2025, Advanced Computer Vision Algorithm for Extraction of Microstructural Features from BSE Images of Powder Metallurgical Microstructures. The International Journal of Advanced Manufacturing Technology, 140(7–8): 1–19.

Liao D, Bi R, Zheng Y, et al., 2025, LCW-YOLO: An Explainable Computer Vision Model for Small Object Detection in Drone Images. Applied Sciences, 15(17): 9730–9730.

Najafi J, Mirzakuchaki S, Shamaghdari S, 2025, Autonomous Drone Detection and Classification Using Computer Vision and Prony Algorithm-Based Frequency Feature Extraction. Journal of Intelligent & Robotic Systems, 111(1): 8–8.

Junfeng J, Tian G, Weichuan Z, et al., 2023, Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(4): 4694–4712.

Cedric C, Thomas F, 2023, Computer Vision and Internet Meme Genealogy: An Evaluation of Image Feature Matching as a Technique for Pattern Detection. Communication Methods and Measures, 17(1): 17–39.

Feng Y, 2023, An Intelligent Detection Method of Local Feature Points in Computer Vision Image. International Journal of Information and Communication Technology, 23(3): 266–277.

Xi C, Yali M, ShuHui L, 2023, Vision based Defect Detection Technologies in Civil Structures: A Review Study. Journal of Optics, 53 (2): 1456–1461.

Fang C, Yu S, Li Y, et al., 2024, Deep Learning-Based Computer Vision Health Monitoring for Civil Engineering. Journal of Tongji University (Natural Science), 52(2): 213–222.