AI-Enabled Real-Time Process Monitoring: A Framework for Intelligent Automation in Industrial Systems

Abstract

Industrial manufacturing processes generate vast amounts of data, yet most traditional monitoring systems struggle to keep up — missing anomalies, reacting slowly, and requiring constant human supervision. Real-time monitoring remains challenging due to process complexity and the difficulty of measuring critical quality variables as they occur [1,2]. As automation scales and sensor data volumes grow, rule-based and manual approaches become increasingly inadequate for fault detection and process control [3]. A systematic framework is therefore needed to enable automated monitoring with fewer resources.

This paper proposes a machine learning-based framework for real-time industrial process monitoring. Sensor data is collected and routed through an AI inference pipeline that flags deviations before failures occur. The framework comprises a data pre-processing module, an anomaly detection engine, and an automated alert system — designed to generalize across chemical plants, manufacturing floors, and energy systems without domain-specific rebuilding [4].

AI-driven monitoring reduces human intervention, cuts unplanned downtime, and improves process predictability [5]. This paper also addresses organizational deployment and scaling — the layer where most implementations fail. The work sits at the intersection of AI, process engineering, and industrial automation.

References

[1] L. Cao et al., IEEE Trans. Instrum. Meas., 2025. doi: 10.1109/TIM.2025.3556830

[2] D. Tiwari et al., Proc. Inst. Mech. Eng. B, vol. 235, no. 13, pp. 2035–2051, 2021. doi: 10.1177/09544054211016675

[3] H. Nizam et al., IEEE Sens. J., vol. 22, no. 23, pp. 22836–22849, 2022. doi: 10.1109/JSEN.2022.3211874

[4] M. Pech et al., Sensors, vol. 21, no. 4, p. 1470, 2021. doi: 10.3390/s21041470

[5] A. Jaramillo-Alcazar et al., Sensors, vol. 23, no. 19, p. 8286, 2023. doi: 10.3390/s23198286