International Journal of Computational Intelligence Systems

Volume 7, Issue sup1, January 2014, Pages 68 - 83

Replacement policies for a complex system with unobservable components using dynamic Bayesian networks

Authors
Demet O., Taner Bilgiç
Corresponding Author
Demet O.
Available Online 9 January 2017.
DOI
https://doi.org/10.1080/18756891.2014.853933How to use a DOI?
Keywords
Maintenace, replacement policy, DBNs, uncertainty modelling, optimization and heuristics
Abstract
We study maintenance of a complex dynamic system consisting of ageing and unobservable components under a predetermined threshold reliability level. Our aim is to construct an optimum replacement policy for the components of the system by minimizing total number of replacements or total replacement cost. We represent the problem with dynamic Bayesian networks (DBNs). We prove that under the existence of a predetermined threshold reliability, performing replacements at periods when the system reliability just falls below the threshold assures optimum replacement times. Four component selection approaches and their cost focused versions are proposed to choose the component to replace and are tested on a complex dynamic problem. Their performances are analyzed under various threshold and cost levels.
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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
7 - 100
Pages
68 - 83
Publication Date
2017/01
ISSN
1875-6883
DOI
https://doi.org/10.1080/18756891.2014.853933How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - Demet O.
AU  - Taner Bilgiç
PY  - 2017
DA  - 2017/01
TI  - Replacement policies for a complex system with unobservable components using dynamic Bayesian networks
JO  - International Journal of Computational Intelligence Systems
SP  - 68
EP  - 83
VL  - 7
IS  - sup1
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2014.853933
DO  - https://doi.org/10.1080/18756891.2014.853933
ID  - O.2017
ER  -