Skip to main content

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

Wu, Shaomin, Scarf, Philip (2017) Two new stochastic models of the failure process of a series system. European Journal of Operational Research, 257 (3). pp. 763-772. ISSN 0377-2217. (doi:10.1016/j.ejor.2016.07.052) (KAR id:56655)

PDF Author's Accepted Manuscript
Language: English

Download (344kB) Preview
[thumbnail of Kent_Wu.pdf]
This file may not be suitable for users of assistive technology.
Request an accessible format
Official URL:


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 the 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 estimation. 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 generalised 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.

Item Type: Article
DOI/Identification number: 10.1016/j.ejor.2016.07.052
Uncontrolled keywords: non-homogeneous Poisson process (NHPP), geometric process (GP), generalised renewal process (GRP), superimposed renewal process, virtual component
Subjects: H Social Sciences > HA Statistics > HA33 Management Science
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Depositing User: Shaomin Wu
Date Deposited: 31 Jul 2016 08:53 UTC
Last Modified: 08 Oct 2021 12:09 UTC
Resource URI: (The current URI for this page, for reference purposes)
Wu, Shaomin:
  • Depositors only (login required):


Downloads per month over past year