Optimal Age Policy for a Used System with Imperfect Preventive Maintenance and Cumulative Damage Model
Yen-Luan Chen
Communications in Statistics - Theory and Methods, 2014, vol. 43, issue 19, 4062-4073
Abstract:
In this article, the concept of imperfect preventive maintenance is discussed and an age maintenance policy is developed based on the cumulative damage model for a used system with initial variable damage. The deterioration of the system is assumed to suffer the non-homogeneous Poisson shocks which can be divided into two types with stochastic probability: Type-I shock (minor) yields a random amount of additive damage of the system, or Type-II shock (catastrophic) causes the system to fail. An age preventive maintenance policy T is presented in which the system undergoes preventive maintenance at a scheduled lifetime T, or corrective maintenance at first Type-II shock and the total damage exceeds a threshold level, whichever occurs first. The objective is to determine the optimal preventive maintenance schedule such that the expected cost rate is minimized. The optimal solution is derived analytically and discussed numerically.
Date: 2014
References: Add references at CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2012.718847 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:43:y:2014:i:19:p:4062-4073
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20
DOI: 10.1080/03610926.2012.718847
Access Statistics for this article
Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe
More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().