Methodology for optimization of component reliability of heat supply systems
Ivan Postnikov,
Valery Stennikov,
Ekaterina Mednikova and
Andrey Penkovskii
Applied Energy, 2018, vol. 227, issue C, 365-374
Abstract:
The paper suggests a methodology to determine optimal reliability parameters (failure and restoration rates) of heat supply system components, which provide the required level of heat supply reliability. The methodological approach consists in the economically rational distribution of the total effect of reliability improvement among the system components, which is calculated using the average reliability parameters of the components. This task, along with the task to ensure structural reliability, is one of the key reliability tasks within a more general problem of optimal synthesis of heat supply systems and is urgent for both the systems under design and the existing insufficiently reliable systems.
Keywords: Heat supply system; Reliability optimization; Nodal reliability indices; Component reliability; Failure and restoration rates; Markov random process (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261917316720
Full text for ScienceDirect subscribers only
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:eee:appene:v:227:y:2018:i:c:p:365-374
Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic
DOI: 10.1016/j.apenergy.2017.11.073
Access Statistics for this article
Applied Energy is currently edited by J. Yan
More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().