An expert system for monitoring the status of control systems

Abstract

A real-time expert system, consisting of a knowledge base, an inference engine, and a linear predictive coding algorithm (LPCA), is proposed for monitoring the status of the control system. The ARMA (autoregressive moving average) model estimates are used to compute the performance and stability measures, predict overload, and detect faults. The control system is asserted to be in (1) normal state if the performance and stability measures are within the threshold limits, (2) alert state if the performance and/or stability measures violate the limits, and (3) emergency state if the error signal is unbounded. The LPCA is broken into a number of tasks. The expert system controls the execution of these tasks and validates the assertions using heuristic, contextual, and control-theoretic reasoning. The status information is displayed in the order of decreasing importance such that essentials are known earlier with the complete picture emerging later. The proposed scheme is implemented on a commercially available expert system shell. >