Reliability Estimation Based on System Data with an Unknown Load Share Rule

Authors: Hyoungtae Kim¹; Paul H. Kvam²

Source: Lifetime Data Analysis, Volume 10, Number 1, March 2004 , pp. 83-94(12)

Publisher: Springer

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Abstract:

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.

Keywords: maximum likelihood; software reliability; order restricted inference; system dependence

Document Type: Research article

DOI: http://dx.doi.org/10.1023/B:LIDA.0000019257.74138.b6

Affiliations: 1: School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA 2: School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; pkvam@isye.gatech.edu, Email: pkvam@isye.gatech.edu

Publication date: 2004-03-01