A modularized framework for predictive maintenance scheduling
Ming-Yi You and
Guang Meng
Journal of Risk and Reliability, 2012, vol. 226, issue 4, 380-391
Abstract:
This paper presents a modularized, easy-to-implement framework for predictive maintenance scheduling. With a modularization treatment of a maintenance scheduling model, a predictive maintenance scheduling model can be established by integrating components’ real-time, sensory-updated prognostics information with a classical preventive maintenance/condition-based maintenance scheduling model. With the framework, a predictive maintenance scheduling model for multi-component systems is established to illustrate the framework’s use; such a predictive maintenance scheduling model for multi-component systems has not been reported previously in the literature. A numerical example is provided to investigate the individual-orientation and dynamic updating characteristics of the optimal preventive maintenance schedules of the established predictive maintenance scheduling model and to evaluate the performance of these preventive maintenance schedules. It is hoped that the presented framework will facilitate the implementation of predictive maintenance policies in various industrial applications.
Keywords: Condition-based maintenance; predictive maintenance; maintenance scheduling; maintenance optimization (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/1748006X11431209 (text/html)
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:sae:risrel:v:226:y:2012:i:4:p:380-391
DOI: 10.1177/1748006X11431209
Access Statistics for this article
More articles in Journal of Risk and Reliability
Bibliographic data for series maintained by SAGE Publications ().