Machine Learning for Alarm Forecasting and Anomaly Detection in Industrial IoT Environments
Keywords:
Keywords: Alarm Forecasting, Anomaly Detection, Industrial IoT, IIoT, Real-time Monitoring, Predictive Maintenance.Abstract
In industrial Internet of Things (IIoT) environments, efficient monitoring systems are crucial forensuring smooth operations, minimizing downtime, and maintaining safety standards. Traditionalmethods for alarm forecasting and anomaly detection often fall short in handling the vast amounts ofreal-time data generated by IIoT devices, leading to delayed or missed detections of potential issues
References
S. G. Yoon et al., "Anomaly Detection in Industrial IoT Systems Using Convolutional Neural Networks," IEEE Transactions on Industrial Informatics, vol. 17, no. 3, pp. 2117-2126, March 2021.
J. Xie et al., "Machine Learning-Based Alarm Forecasting in Industrial IoT: A Survey and Future Directions," IEEE Access, vol. 10, pp. 24511-24525, 2022
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Published
2025-04-09
How to Cite
Dr. J. Sravanthi, Kommadi Shreya Reddy, Ragam Kalyani, Devaruppula SaiMahesh, Dharmarapu Rakesh. (2025). Machine Learning for Alarm Forecasting and Anomaly Detection in Industrial IoT Environments . Journal of Computational Analysis and Applications (JoCAAA), 34(4), 143–153. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2283
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