A probability model of system downtime with implications for optimal warranty design

Traditional approaches toward modeling the availability of a system often do not formally take into account uncertainty over the parameter values of the model. Such models are then frequently criticized because the observed reliability of a system does not match that predicted by the model. This paper extends a recently published segregated failures model so that, rather than providing a single figure for the availability of a system, uncertainty over model parameter values is incorporated and a predictive probability distribution is given. This predictive distribution is generated in a practical way by displaying the uncertainties and dependencies of the parameters of the model through a Bayesian network (BN). Permitting uncertainty in the reliability model then allows the user to determine whether the predicted reliability was incorrect due to inherent variability in the system under study, or due to the use of an inappropriate model. Furthermore, it is demonstrated how the predictive distribution can be used when reliability predictions are employed within a formal decision‐theoretic framework. Use of the model is illustrated with the example of a high‐availability computer system with multiple recovery procedures. An BN is produced to display the relations between parameters of the model in this case and to generate a predictive probability distribution of the system's availability. This predictive distribution is then used to make two decisions under uncertainty concerning the offered warranty policies on the system: a qualitative decision and an optimization over a continuous decision space.