Reliability assessment of legacy safety-critical systems upgraded with off-the-shelf components

Abstract

Reliability assessment of upgraded legacy systems is an important problem in many safety-related industries. Some parts of the equipment used in the original design of such systems are either not available off-the-shelf (OTS) or have become extremely expensive as a result of being discontinued as mass production components. Maintaining a legacy system, therefore, demands using different OTS components. Trustworthy reliability assurance after an upgrade with a new OTS component is needed which combines the evidence about the reliability of the new OTS component with the knowledge about the old system accumulated to date. In these circumstances Bayesian approach to reliability assessment is invaluable. Earlier studies have used Bayesian inference under simplifying assumptions. Here we study the effect of these on the accuracy of predictions and discuss the problems, some of them open for future research, of using Bayesian inference for practical reliability assessment.