Recursive Polynomial Approach to Fault Prediction in Technical Process Control System

This paper presents an intelligent Process Control System (PCS) that integrates a neuro-logic solver with a recursive polynomial estimator for fault prediction and integrity monitoring in safety-critical environments. The system combines Artificial Neural Networks (ANNs) with recursive polynomial regression to enhance reliability and compliance with IEC 61069 and IEC 61508 standards. Using operational data from a distillation unit, a neural network was trained as the safety logic solver, while an ARMAX-based recursive estimator modeled plant dynamics for early fault detection. Real-time simulation and control were conducted in MATLAB and Simulink. The system achieved a Mean Squared Error of 2.98 × 10⠻⠹, a regression coefficient of 0.9978, and a PFD of 9.00 × 10⠻², corresponding to Safety Integrity Level 4. Results show robust fault tolerance, accurate forecasting, and adaptive control, making the approach well-suited for industrial applications demanding high safety and availability. The limitation of the work is that the system’s performance depends on the quality and representativeness of the training data, and its computational complexity may pose challenges for real-time deployment in resource-constrained environments.