Hierarchical, Combinatorial-Markov Method of Solving Complex Reliability Models

摘要

Combinatorial models such as fault trees and reliability block diagrams are efficient in both specification and evaluation of system models. But it is difficult if not impossible to allow for various types of dependency (such as repair dependency and near coincident fault type dependency), transient and intermittent faults, standby systems with warm spares, and so forth. Markov models can capture such interesting system behavior. However, the size of a Markov model for the evaluation of such a system may grow exponentially with the number of components in the system. This reprint presents a modeling tool called SHARPE (Symbolic Hierarchical Automated Reliability and Performance Evaluator), which is a general hierarchical modeling tool for analyzing complex models. SHARPE allows it users complete freedom to choose the number of levels of models and the type of model (combinatorial or Markov) at each level. Thus it allows the flexibility of Markov models where necessary and retains the efficiency of combinatorial solution where possible. The analysis of each model produces a probability function that is symbolic in the time variable t.