Integration of behavioral fault-detection models and an intelligent reactive scheduler

Abstract

An integrated approach for monitoring large-scale distributed processes and modifying the control plan in real-time, in reaction to deviations from the planned production schedule, is described. The monitoring is accomplished via behavioral models that represent and manipulate both discrete and continuous process dynamics. Reactive scheduling and control is accomplished by combining artificial intelligence and numeric optimization techniques to minimize the cost of recovery. The feasibility of recovery strategies is checked online using the behavioral models in a simulation mode. A prototype systems has been implemented using CROPS5 (Concurrent real-time OPS5) and C++ and runs on the Mach operating systems. The system is intended for online monitoring and reactive scheduling of continuous-caster steel mills.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>