A Data-Centric Framework for Fault Prediction and Process Optimization in Industry 4.0 Environments

The integration of cyber-physical systems, smart sensors and interconnected devices are making industrial processes more and more complex. The conventional monitoring and control systems are sometimes unable to process large-scale real-time data efficiently, resulting in delayed decision-making and increased operational expenses.. The rise of Industry 4.0 has resulted in the rapid deployment of Industrial Internet of Things (IIoT) technologies to monitor and control processes in real-time. But communication latency, poor model interpretability and delayed anomaly detection still plague industrial environments, even with the proliferation of smart sensors and cloud-based analytics. To overcome these challenges, in this paper, we propose an Explainable Edge-AI framework that synergistically combines smart sensing, edge computing, machine learning, and predictive control for efficient industrial process management.

The system we propose will collect data from heterogeneous sensor networks and run lightweight ML models at the edge to detect anomalies, predict process deviations, and trigger adaptive control actions. We incorporate Explainable AI (XAI) methods for increasing prediction transparency and operator trust. A digital twin of the process environment is also created to validate performance and optimize system behavior.

The experimental results demonstrate that the fault detection accuracy, system responsiveness, and overall control performance, are improved by 96%, 200 ms, and 30%, respectively, when compared to traditional cloud-centric architectures. The framework offers a scalable, intelligent and interpretable solution for the next generation smart manufacturing system