Bayesian aggregation of experts' judgements on failure intensity

Tony Rosqvist

Research output: Contribution to journalArticleScientificpeer-review

17 Citations (Scopus)

Abstract

Predictive modelling in the field of dependability, such as repair cost modelling, is usually based on the failure intensity function of repairable systems. The experience feedback data needed to estimate the failure intensity function is, however, seldom available due to the unique character of the systems. Thus approaches that can capture the tacit knowledge of designers, operators and maintenance personnel and transform this to the mathematical format required in the predictive models, are needed. A Bayesian statistical approach is presented, which yields posterior distributions of the parameters of the Power Law and the Log-Linear intensity functions, which are used to model the trend in the data observed. Model-checking criteria are presented according to which the relative performance of the selected functions can be assessed. Numerical examples are also given.

Original languageEnglish
Pages (from-to)283 - 289
Number of pages7
JournalReliability Engineering and System Safety
Volume70
Issue number3
DOIs
Publication statusPublished - 2000
MoE publication typeA1 Journal article-refereed

Fingerprint

Agglomeration
Model checking
Repair
Personnel
Feedback
Costs

Cite this

@article{108d7b88916d4f4d949e35919186c15d,
title = "Bayesian aggregation of experts' judgements on failure intensity",
abstract = "Predictive modelling in the field of dependability, such as repair cost modelling, is usually based on the failure intensity function of repairable systems. The experience feedback data needed to estimate the failure intensity function is, however, seldom available due to the unique character of the systems. Thus approaches that can capture the tacit knowledge of designers, operators and maintenance personnel and transform this to the mathematical format required in the predictive models, are needed. A Bayesian statistical approach is presented, which yields posterior distributions of the parameters of the Power Law and the Log-Linear intensity functions, which are used to model the trend in the data observed. Model-checking criteria are presented according to which the relative performance of the selected functions can be assessed. Numerical examples are also given.",
author = "Tony Rosqvist",
year = "2000",
doi = "10.1016/S0951-8320(00)00064-8",
language = "English",
volume = "70",
pages = "283 -- 289",
journal = "Reliability Engineering and System Safety",
issn = "0951-8320",
publisher = "Elsevier",
number = "3",

}

Bayesian aggregation of experts' judgements on failure intensity. / Rosqvist, Tony.

In: Reliability Engineering and System Safety, Vol. 70, No. 3, 2000, p. 283 - 289.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Bayesian aggregation of experts' judgements on failure intensity

AU - Rosqvist, Tony

PY - 2000

Y1 - 2000

N2 - Predictive modelling in the field of dependability, such as repair cost modelling, is usually based on the failure intensity function of repairable systems. The experience feedback data needed to estimate the failure intensity function is, however, seldom available due to the unique character of the systems. Thus approaches that can capture the tacit knowledge of designers, operators and maintenance personnel and transform this to the mathematical format required in the predictive models, are needed. A Bayesian statistical approach is presented, which yields posterior distributions of the parameters of the Power Law and the Log-Linear intensity functions, which are used to model the trend in the data observed. Model-checking criteria are presented according to which the relative performance of the selected functions can be assessed. Numerical examples are also given.

AB - Predictive modelling in the field of dependability, such as repair cost modelling, is usually based on the failure intensity function of repairable systems. The experience feedback data needed to estimate the failure intensity function is, however, seldom available due to the unique character of the systems. Thus approaches that can capture the tacit knowledge of designers, operators and maintenance personnel and transform this to the mathematical format required in the predictive models, are needed. A Bayesian statistical approach is presented, which yields posterior distributions of the parameters of the Power Law and the Log-Linear intensity functions, which are used to model the trend in the data observed. Model-checking criteria are presented according to which the relative performance of the selected functions can be assessed. Numerical examples are also given.

U2 - 10.1016/S0951-8320(00)00064-8

DO - 10.1016/S0951-8320(00)00064-8

M3 - Article

VL - 70

SP - 283

EP - 289

JO - Reliability Engineering and System Safety

JF - Reliability Engineering and System Safety

SN - 0951-8320

IS - 3

ER -