Bayesian Methodology for Reliability Growth Planning and Projection for Discrete-Use Systems Utilizing Multi-Source Data

This research aims to present a Bayesian model for reliability growth projection and planning for discrete-use systems suitable for use throughout all stages of system development. Traditional discrete-use models for reliability growth utilize test data from individual test events at the current stage of development. They often neglect the inclusion of historical information from previous tests, testing similar systems, or eliciting expert opinion. Examining and using data attained from prior bench analyses, sub-system tests, or user trial events often fails to occur or is conducted poorly. Additionally, no current approach permits the probabilistic treatment of the initial system reliability at the commencement of the test program in conjunction with the management variables that may change throughout the execution of the test plan.This research aims to contribute to the literature in several ways. Firstly, a new Bayesian model is developed from first principles, which considers the uncertainty surrounding discrete-use systems under arbitrary corrective action regimes to address failure modes. This differs from current models that fail to address the randomized times that corrective actions to observed failure modes may be implemented depending on the selected management strategy. Some current models only utilize the first observed failure on test, meaning a significant loss of information transpires if subsequent failures are ignored. Additionally, the proposed strategy permits a probabilistic assessment of the test program, accounting for uncertainty in several management variables. The second contribution seeks to extend the Bayesian discrete-use system projection model by considering aspects of developmental, acceptance, and operational testing to formulate a holistic reliability growth plan framework that extends over the entire system lifecycle. The proposed approach considers the posterior distribution from each phase of reliability growth testing as the prior for the following growth test event. The same methodology is then employed using the posterior from the final phase of reliability growth testing as the prior for acceptance testing. It then follows that the acceptance testing posterior distribution forms the prior for subsequent operational testing through a Bayesian learning method. The approach reduces unrealistic and unattainable reliability demonstration testing that may result from a purely statistical analysis. The proposed methodology also permits planning for combined developmental and acceptance test activities within a financially constrained context. Finally, the research seeks to define an approach to effectively communicate developmental system reliability growth plans and risks to decision makers. Like many of their other specialist science peers, reliability professionals are fantastic communicators – with other reliability practitioners. However, when reliability professionals move beyond their world to make an impact, they often face the same challenge scientists from every discipline face – the difficulties of clearly communicating science to their audience