Fuzzy Weibull for risk analysis

Reliability and the life of components are frequently prime safety considerations. Extensive qualitative analysis employing probabilistic risk assessment has been widely performed to minimize hazards or accidents. Weibull probability data and information is a vital tool of quantitative risk assessments, but so are qualitative methods such as fault tree analysis. Qualitative aspects of product risk are subjective and contain many uncertainties. Most risk analyses do not have a means of dealing with uncertainty. Fuzzy set theoretical methods deal with supporting reasoning with uncertainty efficiently and conclusively. A unique fuzzy logic method employing Weibulls to represent membership functions for a set of fuzzy values along with “crisp” values has been developed for addressing uncertainties. The paper describes a type of AI software for risk analysis. A complete description is presented with parallels to previous methods. The model discussed is called fuzzy fault tree (FFT). It employs “fuzzy Weibull” membership functions which have been demonstrated in a working prototype. The prototype system which provides maximum utility in minimizing risk uncertainties is programmed in C for Apple Macintosh platforms. The results validate this application of fuzzy logic to qualitative risk assessment modeling, and lend credibility to the validity of the approach. The fuzzy Weibulls used in the modeling process perform quite well. Before developing FFT into an operational system, several calibration trials with a variety of risk assessment problems will be attempted