Two new stochastic models of the failure process of a series system

Wu, S; Scarf, P

Abstract

Consider a series system consisting of sockets into each of
which a component is inserted: if a component fails, it is replaced with
a new identical one immediately and system operation resumes. An
interesting question is: how to model the failure process
of the system
as a whole when the lifetime distribution of each component is unknown?
This paper attempts to answer this question by developing two new models,
for the cases of a specified and an unspecified number of sockets,
respectively. It introduces t
he concept of a virtual component, and in
this sense, we suppose that the effect of repair corresponds to
replacement of the most reliable component in the system. It then
discusses the probabilistic properties of the models and methods for
parameter estim
ation. Based on six datasets of artificially generated
system failures and a real
-
world dataset, the paper compares the
performance of the proposed models with four other commonly used models:
the renewal process, the geometric process, Kijima's generalise
d renewal
process, and the power law process. The results show that the proposed
models outperform these comparators on the datasets, based on the Akaike
information criterion.

Citation

Wu, S., & Scarf, P. (2017). Two new stochastic models of the failure process of a series system. European Journal of Operational Research, 257(3), 763-772. https://doi.org/10.1016/j.ejor.2016.07.052

• Journal Article Type: Article
• Acceptance Date: Jul 19, 2016
• Online Publication Date: Jul 29, 2016
• Publication Date: Mar 16, 2017
• Deposit Date: Jul 26, 2016
• Publicly Available Date: Jul 29, 2018
• Journal: European Journal of Operational Research
• Print ISSN: 0377-2217
• Publisher: Elsevier
• Volume: 257
• Issue: 3
• Pages: 763-772
• DOI: https://doi.org/10.1016/j.ejor.2016.07.052
• Publisher URL: http://dx.doi.org/10.1016/j.ejor.2016.07.052
• Related Public URLs: http://www.journals.elsevier.com/european-journal-of-operational-research/
• Additional Information: Projects : SMART-E: Sustainable Manufacturing through Advanced Robotics Training - Europe