Access the full text.
Sign up today, get DeepDyve free for 14 days.
S. Kourniotis, C. Kiranoudis, N. Markatos (2000)
Statistical analysis of domino chemical accidents.Journal of hazardous materials, 71 1-3
T. Satow, S. Osaki (2003)
Optimal replacement policies for a two-unit system with shock damage interactionComputers & Mathematics With Applications, 46
Chien-Kuo Sung, S. Sheu, Tsung-Shin Hsu, Yan-Chun Chen (2013)
Extended optimal replacement policy for a two-unit system with failure rate interaction and external shocksInternational Journal of Systems Science, 44
J. Jhang, S. Sheu (2000)
Optimal age and block replacement policies for a multi-component system with failure interactionInternational Journal of Systems Science, 31
Q. Nguyen, A. Mebarki, R. Saada, F. Mercier, M. Reimeringer (2009)
Integrated probabilistic framework for domino effect and risk analysisAdv. Eng. Softw., 40
V. Cozzani, G. Gubinelli, G. Antonioni, G. Spadoni, S. Zanelli (2005)
The assessment of risk caused by domino effect in quantitative area risk analysis.Journal of hazardous materials, 127 1-3
J. Filus (1991)
On a type of dependency between Weibull lifetimes of system componentsReliability Engineering & System Safety, 31
G. Pettitt, R. Schumacher, Louise Seeley (1993)
Evaluating the probability of major hazardous incidents as a result of escalation eventsJournal of Loss Prevention in The Process Industries, 6
Min-Tsai Lai, Ying-Chang Chen (2006)
Optimal periodic replacement policy for a two-unit system with failure rate interactionThe International Journal of Advanced Manufacturing Technology, 29
Min-Tsai Lai, John Yuan (1991)
Periodic replacement model for a parallel system subject to independent and common cause shock failuresReliability Engineering & System Safety, 31
L. Thomas (1986)
A survey of maintenance and replacement models for maintainability and reliability of multi-item systemsReliability Engineering, 16
G. Apostolakis, P. Moieni (1987)
The foundations of models of dependence in probabilistic safety assessmentReliability Engineering, 18
D. Murthy, D. Nguyen (1985)
Study of two‐component system with failure interactionNaval Research Logistics Quarterly, 32
D. Murthy, Richard Wilson (1994)
Parameter estimation in multi–component systems with failure interactionApplied Stochastic Models and Data Analysis, 10
T. Nakagawa, D. Murthy (1993)
Optimal replacement policies for a two-unit system with failure interactionsRairo-operations Research, 27
A. Marshall, I. Olkin (1967)
A Multivariate Exponential DistributionJournal of the American Statistical Association, 62
Min-Tsai Lai, Huey Yan (2016)
Optimal number of minimal repairs with cumulative repair cost limit for a two-unit system with failure rate interactionsInternational Journal of Systems Science, 47
S. Albin, S. Chao (1992)
Preventive replacement in systems with dependent componentsIEEE Transactions on Reliability, 41
U. Rakowsky, W. Schneeweiss (2004)
Modelling Dependent Component Failures with Domino Effects
R. Zequeira, C. Bérenguer (2005)
On the inspection policy of a two-component parallel system with failure interactionReliab. Eng. Syst. Saf., 88
G. Greig (1993)
Second moment reliability analysis of redundant systems with dependent failuresReliability Engineering & System Safety, 41
J. Freund (1961)
A Bivariate Extension of the Exponential DistributionJournal of the American Statistical Association, 56
P. Scarf, M. Deara (2003)
Block replacement policies for a two‐component system with failure dependenceNaval Research Logistics (NRL), 50
Purpose – The purpose of this paper is to investigate age replacement policies for two-component parallel system with stochastic dependence. The stochastic dependence considered, is modeled by a one-sided domino effect. The failure of component 1 at instant t may induce the failure of component 2 at instant t + τ with probability p 1→2 . The time delay τ is a random variable with known probability density function h p 1→2 (.). The system is considered in a failed state when both components are failed. The proposed replacement policies suggest to replace the system upon failure or at age T whichever occurs first. Design/methodology/approach – In the first policy, costs and durations associated with maintenance activities are supposed to be constant. In the second replacement policy, the preventive replacement cost depends on the system’s state and age. The expected cost per unit of time over an infinite span is derived and numerical examples are presented. Findings – In this paper and especially in the second policy, the authors find that the authors can get a more economical policy if the authors consider that the preventive replacement cost is not constant but depends on T . Originality/value – In this paper, the authors take into account of the stochastic dependence between system components. This dependence affects the global reliability of the system and replacement’s periodicity. It can be used to measure the performance of the system et introduced into design phase of the system.
Journal of Quality in Maintenance Engineering – Emerald Publishing
Published: Aug 10, 2015
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.