Indirect assessment of system reliability
Assessing system-reliability has been a major goal of many people in many walks of life, including those that develop and deploy systems. For that purpose many methods and techniques have been published. However, most of these techniques and methods rely heavily on failure data. Because the design of high-reliability systems generally requires that the individual system components have extremely high reliability, even after long periods of time, and because short product-development times and tightened-budgets imply that reliability tests must be conducted with severe time-constraints, frequently no failures occur during such tests. Thus, there is a growing need to assess reliabilities for complex systems for which it is difficult to collect failure data. This paper focuses on a method that expresses the failure time and consequently the reliability of a system as a function of several explanatory variables (referred to as covariates). Then, the system reliability is assessed indirectly by using information on these covariates. From a practical viewpoint, this method might be more complicated and difficult to apply. The author still recommends this methodology because there are many situations where it is difficult to have data on failure times of expensive and complex systems, and this method can handle such situations.
cumulative damage model diffusion process hitting time ito process log-normal process maximum likelihood estimate stochastic process stress and strength system