Campus Units

Statistics

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

2017

Journal or Book Title

Technometrics

Volume

59

Issue

2

First Page

202

Last Page

214

DOI

10.1080/00401706.2016.1172028

Abstract

Maintenance data can be used to make inferences about the lifetime distribution of system components. Typically, a fleet contains multiple systems. Within each system, there is a set of nominally identical replaceable components of particular interest (e.g., 2 automobile headlights, 8 dual in-line memory module (DIMM) modules in a computing server, 16 cylinders in a locomotive engine). For each component replacement event, there is system-level information that a component was replaced, but no information on which particular component was replaced. Thus, the observed data are a collection of superpositions of renewal processes (SRP), one for each system in the fleet. This article proposes a procedure for estimating the component lifetime distribution using the aggregated event data from a fleet of systems. We show how to compute the likelihood function for the collection of SRPs and provide suggestions for efficient computations. We compare performance of this incomplete-data maximum likelihood (ML) estimator with the complete-data ML estimator and study the performance of confidence interval methods for estimating quantiles of the lifetime distribution of the component. Supplementary materials for this article are available online.

Comments

This is an Accepted Manuscript of an article published by Taylor & Francis as Zhang, Wei, Ye Tian, Luis A. Escobar, and William Q. Meeker. "Estimating a parametric component lifetime distribution from a collection of superimposed renewal processes." Technometrics 59, no. 2 (2017): 202-214. DOI: 10.1080/00401706.2016.1172028. Posted with permission.

Copyright Owner

American Statistical Association and the American Society for Quality

Language

en

File Format

application/pdf

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