We consider a multicomponent load-sharing system in which the failure rate of a given component depends on the set of working components at any given time. Such systems can arise in software reliability models and in multivariate failure-time models in biostatistics, for example. A load-share rule dictates how stress or load is redistributed to the surviving components after a component fails within the system. In this paper, we assume the load share rule is unknown and derive methods for statistical inference on load-share parameters based on maximum likelihood. Components with (individual) constant failure rates are observed in two environments: (1) the system load is distributed evenly among the working components, and (2) we assume only the load for each working component increases when other components in the system fail. Tests for these special load-share models are investigated.
Copyright © 2004 Kluwer Academic Publishers.
The definitive version is available at: https://link.springer.com/article/10.1023/B%3ALIDA.0000019257.74138.b6
Kim, Hyoungtae, and Paul H. Kvam. "Reliability Estimation Based on System Data with an Unknown Load Share Rule." Lifetime Data Analysis 10, no. 1 (2004): 83-94. doi:10.1023/b:lida.0000019257.74138.b6.
Kim, Hyoungtae and Kvam, Paul H., "Reliability Estimation Based on System Data with an Unknown Load Share Rule" (2004). Math and Computer Science Faculty Publications. 194.