ABSTRACT
This chapter applies the methods to three examples: a simple fault tree used to illustrate the fusion and propagation algorithm in Dempster and Kong, a simple crossed fault tree with 4 components, which exhibits information dependence, and the typical Probabilistic Risk Assessment fault tree found in Spencer, Diegert, and Easterling. The first two examples are artificial, but the last example is a piece of the Interim Reliability Evaluation Program risk analysis, and thus illustrates the application of belief functions to a real world problem. The chapter explores some of the subsystem failure data to find high risk and poorly understood subsystems. It produces two estimates for the rate of Loss of Coolant Accidents, one using “nominal” values for the inputs, and one using the Monte Carlo estimates. Both estimates are of the same order of magnitude and are consistent with the results reported in Spencer, Diegert, and Easterling using the Maximus method.
