Reliability Modeling Using SHARPE

Abstract

Combinatorial models such as fault trees and reliability block diagrams are efficient for model specification and often efficient in their evaluation. But it is difficult, if not impossible, to allow for dependencies (such as repair dependency and near-coincident-fault type dependency), transient and intermittent faults, standby systems with warm spares, and so on. Markov models can capture such important system behavior, but the size of a Markov model can grow exponentially with the number of components in this system. This paper presents an approach for avoiding the large state space problem. The approach uses a hierarchical modeling technique for analyzing complex reliability models. It allows the flexibility of Markov models where necessary and retains the efficiency of combinatorial solution where possible. Based on this approach a computer program called SHARPE (Symbolic Hierarchical Automated Reliability and Performance Evaluator) has been written. The hierarchical modeling technique provides a very flexible mechanism for using decomposition and aggregation to model large systems; it allows for both combinatorial and Markov or semi-Markov submodels, and can analyze each model to produce a distribution function. The choice of the number of levels of models and the model types at each level is left up to the modeler. Component distribution functions can be any exponential polynomial whose range is between zero and one. Examples show how combinations of models can be used to evaluate the reliability and availability of large systems using SHARPE.