Nonparametric estimation and bootstrap confidence intervals for the optimal maintenance time of a repairable system

被引：7

作者：

Gilardoni, Gustavo L. ^{[1
]}

de Oliveira, Maristela D. ^{[2
]}

Colosimo, Enrico A. ^{[3
]}

机构：

[1] Univ Brasilia, BR-70910900 Brasilia, DF, Brazil

[2] Univ Fed Bahia, BR-41170290 Salvador, BA, Brazil

[3] Univ Fed Minas Gerais, Belo Horizonte, MG, Brazil

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2013年 / 63卷

关键词：

Bounded intensity models; Constrained maximum likelihood estimation; Greatest convex minorant; Minimal repair; Poisson process; Power law process; INTENSITY FUNCTION; RELIABILITY; INFERENCE;

D O I：

10.1016/j.csda.2013.02.006

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Consider a repairable system operating under a maintenance strategy that calls for complete preventive repair actions at pre-scheduled times and minimal repair actions whenever a failure occurs. Under minimal repair, the failures are assumed to follow a nonhomogeneous Poisson process with an increasing intensity function. This paper departs from the usual power-law-process parametric approach by using the constrained nonparametric maximum likelihood estimate of the intensity function to estimate the optimum preventive maintenance policy. Several strategies to bootstrap the failure times and construct confidence intervals for the optimal maintenance periodicity are presented and discussed. The methodology is applied to a real data set concerning the failure histories of a set of power transformers. (C) 2013 Elsevier B.V. All rights reserved.

引用

页码：113 / 124

页数：12

共 28 条

[1] NONPARAMETRIC INFERENCE FOR A FAMILY OF COUNTING PROCESSES [J].