From fault diagnosis to corrective action: A support system for industrial process operators

Accurate fault detection and diagnosis are essential in chemical processes, but identifying a fault is only the first step. Once an abnormal event is detected, operators must implement corrective actions to mitigate its effects. This paper presents an operator advisory framework that links online fault diagnosis directly to the generation of optimal corrective actions. The approach integrates a Transformer-based classifier with fault-specific surrogate models to ensure that the recommendations accurately reflect the process dynamics under faulty conditions. To reduce decision complexity, the system employs an interpretable variable selection method to identify the most effective operational levers. Corrective actions are computed via a discrete, time-staggered optimization scheme designed to mitigate the fault progression. Furthermore, if the predicted trajectory drifts from the actual plant response, a plan-deviation monitor recalculates the strategy to provide the operator with updated corrective actions. Functioning purely as a decision-support tool, the system recommends these actions to the operational team, who retains full decision-making authority over their execution on the process. Validation on a non-isothermal CSTR and the Tennessee Eastman Process (TEP) demonstrates the practical viability of the approach. Across 12 complex TEP scenarios, the proposed framework achieves an average operational cost reduction of 15.9%. The system dynamically adapts the corrective action updates for the operator based on the evolution of the fault.

Mots clés

• AI-based Problem Solving and Planning
• Artificial Intelligence
• Control and Systems Engineering
• Fault Detection and Control Systems
• Industrial Automation and Control Systems