Failure rate analysis using GLIMMIX

摘要

This paper illustrates use of a recently developed SAS macro, GLIMMIX, for implementing an analysis suggested by Wolfinger and O'Connell (1993) in modeling failure count data with random as well as fixed factor effects. Interest in this software tool arose from consideration of modernizing the Failure Rate Analysis Code (FRAC), developed at Los Alamos National Laboratory in the early 1980's by Martz, Beckman and McInteer (1982). FRAC is a FORTRAN program developed to analyze Poisson distributed failure count data as a log-linear model, possibly with random as well as fixed effects. These statistical modeling assumptions are a special case of generalized linear mixed models, identified as GLMM in the current statistics literature. In the nearly 15 years since FRAC was developed, there have been considerable advances in computing capability, statistical methodology and available statistical software tools allowing worthwhile consideration of the tasks of modernizing FRAC. In this paper, the approaches to GLMM estimation implemented in GLIMMIX and in FRAC are described and a comparison of results for the two approaches is made with data on catastrophic time-dependent pump failures from a report by Martz and Whiteman (1984). Additionally, statistical and graphical model diagnostics are suggested and illustrated with the GLIMMIX analysis results.