An analytical method is presented which enables one to propagate uncertainties described by continuous probability density functions through fault trees from the lower level (basic event) to the higher level (top-event) of a stochastic binary system. It is based on calculating the expected value and the variance of the top-event probability by means of Binary Decision Diagrams (BDD). This method allows an accurate computation of both the expected value and the variance of the top-event probability. We show, on a benchmark of real fault trees, that our method results in a quantitative and qualitative improvement in safety analysis of industrial systems, especially those concerning accurate evaluation of Safety Integrity Levels (SIL), whenever different sources of uncertainties are present. The numerical results of the analytical method are in good agreement with those of the Monte Carlo method.

Many efforts are currently devoted to the development of soft-computing diagnostic tools. Within these efforts, the present work illustrates the application of a fuzzy logic system approach to fault classification in the case of single and multiple failures of different intensity. The approach bears the great advantage that the identification task does not rely on an explicit mathematical model of the behavior of the monitored plant, the if-then rules of the fuzzy classification model being inferred from available preclassified signal data. A case study is presented regarding the classification of faults in the gland seal system of a pump of the primary heat transport system in a pressurized heavy water reactor, Canada deuterium uranium 6.