87ada507-6f16-44a0-bab7-be895186cfa620250903063748756wseas:wseasmdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS2224-28991109-952610.37394/23207http://wseas.org/wseas/cms.action?id=40161720251720252210.37394/23207.2025.22https://wseas.com/journals/bae/2025.phpJihwanJunDept. of Smart Factory Convergence, Sungkyunkwan University, Suwon, Gyeonggi-do, REPUBLIC OF KOREATae-YongKimDept. of Smart Factory Convergence, Sungkyunkwan University, Suwon, Gyeonggi-do, REPUBLIC OF KOREAJieunLeeDept. of Smart Factory Convergence, Sungkyunkwan University, Suwon, Gyeonggi-do, REPUBLIC OF KOREATaeheonJinDept. of Smart Factory Convergence, Sungkyunkwan University, Suwon, Gyeonggi-do, REPUBLIC OF KOREASeungminParkDept. of Smart Factory Convergence, Sungkyunkwan University, Suwon, Gyeonggi-do, REPUBLIC OF KOREAJongpilJeongDept. of Smart Factory Convergence, Sungkyunkwan University, Suwon, Gyeonggi-do, REPUBLIC OF KOREATraditional camera maintenance methods rely on periodic manual inspections, which are time consuming, costly, and inherently limited in that they only allow problems to be detected and addressed after they occur. In large-scale industrial sites operating hundreds of cameras, such methods frequently lead to human errors and delays in response, resulting in decreased safety and productivity. This study proposes an automated and intelligent predictive maintenance system by integrating artificial intelligence (AI)-based video analysis technology with industrial Internet of Things (IIoT) systems to overcome these limitations. The proposed system collects highresolution video data in real-time and operates through a series of processes including image preprocessing, feature extraction, anomaly detection, severity classification, and maintenance alert transmission. Utilizing CNN-based deep learning algorithms and OpenCV image processing techniques, the system can automatically detect issues such as lens contamination, focus blur, and image degradation. When anomalies are identified, they are immediately classified, and alerts are sent in real-time via a cloudbased notification system. Additionally, maintenance history is automatically logged and analyzed in a database, supporting the development of long-term asset management strategies. Experimental results in real industrial environments demonstrate that the proposed system improves detection accuracy by over 90–95% compared to manual inspection methods, reduces alert response time to within seconds, and lowers maintenance time and costs by more than 70% and 40%, respectively. This research validates the practical effectiveness of automated and predictive maintenance in camera systems as a core technology for smart factory implementation and is expected to contribute to the development of more scalable maintenance frameworks through integration of multi-sensor data and further advancements in predictive algorithms. Index Terms—Predictive Maintenance, Industrial IoT (IIoT), Automated Camera, AI Image Analysis, SmartFactory.93202593202519551970156https://wseas.com/journals/bae/2025/d165107-044(2025).pdf10.37394/23207.2025.22.156https://wseas.com/journals/bae/2025/d165107-044(2025).pdfZhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. ”Intelligent manufacturing in the context of Industry 4.0: A review.” Engineering 3.5 (2017): 616–630. Wischow, M., Gallego, G., Ernst, I., & Borner, A. ”Camera condition monitoring and readjustment by means of noise and blur.” arXiv preprint arXiv:2112.05456 (2021). Lee, J., Bagheri, B., & Jin, C. ”Introduction to cyber manufacturing.” Manufacturing Letters 8 (2016): 11–15. Mobley, R. K. *An Introduction to Predictive Maintenance.* Elsevier, 2002. 10.1016/j.ymssp.2005.09.012Jardine, A. K. S., Lin, D., & Banjevic, D. ”A review on machinery diagnostics and prognostics implementing condition-based maintenance.” Mechanical Systems and Signal Processing 20.7 (2006): 1483–1510. Chen, Z., Wu, M., Zhao, R., Guretno, F., Yan, R., & Li, X. ”Machine remaining useful life prediction via an attention based deep learning approach.” IEEE Transactions on Industrial Electronics 68.3 (2021): 2521–2531. 10.1109/tii.2014.2300753Xu, L. D., He, W., & Li, S. ”Internet of Things in industries: A survey.” IEEE Transactions on Industrial Informatics 10.4 (2014): 2233–2243. Zonta, T., et al. ”Predictive maintenance in the Industry 4.0: A systematic literature review.” Computers & Industrial Engineering 150 (2020): 106889. Achouch, M., et al. ”On predictive maintenance in Industry 4.0: Overview, models, and challenges.” Applied Sciences 12.16 (2022): 8081. Hector, I., & Panjanathan, R. ”Predictive maintenance in Industry 4.0: A survey of planning models and machine learning techniques.” PeerJ Computer Science 10 (2024): e2016. Cummins, L., et al. ”Explainable predictive maintenance: A survey of current methods, challenges and opportunities.” IEEE Access 12 (2024): 57574–57602. Centomo, S., Dall’ Ora, N., & Fummi, F. ”The design of a digital-twin for predictive maintenance.” In Proc. IEEE ETFA, Vienna, Austria, 2020: 1781–1788. Malta, A., Farinha, J. T., & Mendes, M. ”Augmented reality in maintenance—History and perspectives.” Journal of Imaging 9.7 (2023): 142. 10.3390/s25030845Costa, A., et al. ”Smart maintenance solutions: AR- and VR-enhanced digital twin powered by FIWARE.” Sensors 25.3 (2025): 845. Ranjbar, E., et al. ”Design of an IoT-based system for smart maintenance of medical equipment.” In Proc. Int. Conf. IoT, Isfahan, Iran, 2019: 1–12. Vijayakumar, M., et al. ”Intelligent systems for predictive maintenance in industrial IoT.” In Proc. IEEE UPCON, India, 2023: 1650–1655. Alexandru, A. M., et al. ”Building a smart maintenance architecture using smart devices: A Web 2.0 based approach.” In Proc. IEEE RTSI, Bologna, Italy, 2016: 1–6. Giliyana, S., Bengtsson, M., & Salonen, A. ”Implementing and using smart maintenance technologies: Introducing challenges and enablers.” Procedia Computer Science 253 (2025): 932–941. Shajahan, J. M. A., Reyes, S. M., & Xiao, J. ”Camera lens dust detection and dust removal for mobile robots in dusty fields.” In Proc. IEEE ROBIO, China, 2021: 687–691. Das, A., et al. ”TiledSoilingNet: Tile-level soiling detection on automotive surround-view cameras.” In Proc. IEEE ITSC, Greece, 2020: 1–6. Li, X., Yu, S., Lei, Y., Li, N.,& Yang, B. ”Intelligent machinery fault diagnosis with event-based camera.” IEEE Trans. Industrial Informatics 20.1 (2024): 380–389. Liu, F., Tang, J., Yang, J., & Wang, H. ”Automated industrial crack inspection using edge collaboration of multiple cameras.” In Proc. IEEE BMSB, China, 2023: 1–4. Rosell, A., et al. ”Machine learning-based system to automate visual inspection in aerospace engine manufacturing.” In Proc. IEEE ETFA, Romania, 2023: 1–8. 10.1109/34.56205Perona, P., & Malik, J. ”Scale-space and edge detection using anisotropic diffusion.” IEEE TPAMI 12.7 (1990): 629–639. Pizer, S. M., et al. ”Adaptive histogram equalization and its variations.” CVGIP 39.3 (1987): 355–368. Canny, J. ”A computational approach to edge detection.” IEEE TPAMI 8.6 (1986): 679–698. Gonzalez, R. C., & Woods, R. E. *Digital Image Processing.* 4th ed., Pearson, 2018. Jain, A. K. *Fundamentals of Digital Image Processing.* Prentice-Hall, 1989. Brox, T., et al. ”High accuracy optical flow estimation based on a theory for warping.” In Proc. ECCV, 2004: 25–36. Lowe, D. G. ”Distinctive image features from scale-invariant keypoints.” IJCV 60.2 (2004): 91–110. Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. ”Speeded-Up Robust Features (SURF).” CVIU 110.3 (2008): 346–359. Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. ”ORB: An efficient alternative to SIFT or SURF.” In Proc. IEEE ICCV, Spain, 2011: 2564–2571. Calonder, M., Lepetit, V., Strecha, C., & Fua, P. ”BRIEF: Binary robust independent elementary features.” In Proc. ECCV, Greece, 2010: 778–792. Leutenegger, S., Chli, M., & Siegwart, R. Y. ”BRISK: Binary robust invariant scalable keypoints.” In Proc. IEEE ICCV, Spain, 2011: 2548–2555. Hosseini, A., et al. ”A real-time monitoring and alerting system for predictive maintenance in smart factories.” Sensors 20.18 (2020): 5195. Xu, J., et al. ”Edge intelligence for predictive maintenance in IIoT.” IEEE Internet of Things Journal (2021). Yin, C., et al. ”Real-time anomaly detection in industrial settings using machine vision.” Journal of Manufacturing Systems 59 (2021): 252–264. Wang, D., et al. ”Data-driven predictive maintenance scheduling via cloud infrastructure.” Computers in Industry 118 (2020): 103252. Zuehlke, R. ”SmartFactory—Towards a factory-of-things.” Annual Reviews in Control 34.1 (2010): 129–138. Hassan, M., Khan, R., et al. ”Smart alert systems in video surveillance using deep learning.” Journal of Visual Communication and Image Representation 82 (2022): 103513.