Bayes estimation of component-reliability from masked system-life data

This paper estimates component reliability from masked series-system life data, viz, data where the exact component causing system failure might be unknown, It focuses on a Bayes approach which considers prior information on the component reliabilities. In most practical settings, prior engineering knowledge on component reliabilities is extensive, Engineers routinely use prior knowledge and judgment in a variety of ways, The Bayes methodology proposed here provides a formal, realistic means of incorporating such subjective knowledge into the estimation process, In the event that little prior knowledge is available, conservative or even non-informative priors, can be selected. The model is illustrated for a 2-component series system of exponential components, In particular it uses discrete-step priors because of their ease of development & interpretation, By taking advantage of the prior information, the Bayes point-estimates consistently perform well, ie, are close to the MLE, While the approach is computationally intensive, the calculations can be easily computerized.