DYNAMIC-SYSTEMS RELIABILITY EVALUATION USING UNCERTAINTY TECHNIQUES FOR PERFORMANCE MONITORING

The authors suggest the use of fuzzy measures and fuzzy integrals in e valuating the reliability of control systems by approximating an exper ts view on a complex system when assessing the performance. The class of systems considered have structural complexity exhibiting a closed-f orm model of the underlying process. The approach may be described in three parts where in the first stage a rule-based classifier ('spy') e xtracts 'states of performance' from the process. It is shown that the rule-premise resembles a possibility based control chart and that the possibilistic version, embedded in a rule-based system, offers a comp rehensive man-process interface while having a similar or slightly imp roved speed of detection. The reliability can be quantified based on a finite set of abstract states over which a certainty measure is defin ed. A prediction for a specified reliability interval of time is done by using a qualitative model akin to Markov stochastic processes and c onsequently decisions are made to alter the system structure. This fra mework allows distinct classes of uncertainty to be considered.