AI-Assisted Fault Prediction and Adaptive Control in Networked Industrial Automation Systems

  • Authors

    • Subash Chandran Independent Researcher, USA. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-V9I2P104

    Published 04-12-2026

  • Adaptive Control, Artificial Intelligence, Condition Monitoring, Fault Diagnosis, Industrial Automation, Industrial Internet Of Things, Networked Control Systems, Predictive Maintenance, Temporal Deep Learning, And Fault-Tolerant Control

    Issue

    Vol. 9 No. 2 (2026)

    Section

    Articles

  • Abstract

    Networked industrial automation systems play a vital role in manufacturing, intelligent production, and distributed process control in modern times. But the more complex the system is, the more problems there are, like degradation of the equipment, unexpected failures, communication delay, packet loss, and changing operating conditions. Traditional fault detection methods using fixed thresholds and rules might not be able to detect complex patterns of degradation or react to varying conditions. To overcome these challenges, the paper introduces a framework for Fault Prediction and Adaptive Control (AIFP-AC) using Artificial Intelligence (AI) with a combination of sensor analytics, temporal deep learning, probabilistic fault estimation and constrained control optimization. The framework performs equipment measurements and network telemetry processing to forecast potential faults, measure equipment health, and analyze communication quality. The adaptive controller modifies the operating parameters in this manner while respecting the actuator limits, process constraints and safety requirements based on these predictions. A supervisory safety mechanism verifies control actions and initiates fallback strategies if there is more uncertainty. The proposed framework is assessed with fault-prediction accuracy, control performance, communication robustness and equipment availability, for reliable, efficient and resilient industrial automation.

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