Improved reliability modeling using Bayesian networks and dynamic discretization

This paper shows how recent Bayesian network (BN) algorithms can be used to model time to failure distributions and perform reliability analysis of complex systems in a simple unified way. The algorithms work for so-called hybrid BNs, which are BNs that can contain a mixture of both discrete and continuous variables. Our BN approach extends fault trees by defining the time-to-failure of the fault tree constructs as deterministic functions of the corresponding input components' time-to-failure. This helps solve any configuration of static and dynamic gates with general time-to-failure distributions. Unlike other approaches (which tend to be restricted to using exponential failure distributions) our approach can use any parametric or empirical distribution for the time-to-failure of the system components. We demonstrate that the approach produces results equivalent to the state of the practice and art for small examples; more importantly our approach produces solutions hitherto unobtainable for more complex examples, involving non-standard assumptions.. The approach offers a powerful framework for analysts and decision makers to successfully perform robust reliability assessment. Sensitivity, uncertainty, diagnosis analysis, common cause failures and warranty analysis can also be easily performed within this framework.