Dynamic systems reliability evaluation using uncertainty techniques for performance monitoring
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The authors suggest the use of fuzzy measures and fuzzy integrals in evaluating the reliability of control systems by approximating an experts view on a complex system when assessing the performance. The class of systems considered have structural complexity exhibiting a closed-form model of the underlying process. The approach may be described in three parts where in the first stage a rule-based classifier (‘spy’) extracts ‘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 comprehensive man–process interface while having a similar or slightly improved speed of detection. The reliability can be quantified based on a finite set of abstract states over which a certainty measure is defined. A prediction for a specified reliability interval of time is done by using a qualitative model akin to Markov stochastic processes and consequently decisions are made to alter the system structure. This framework allows distinct classes of uncertainty to be considered.
