L z -Transform for a Discrete-State Continuous-Time Markov Process and its Applications to Multi-State System Reliability
Anatoly Lisnianski ()
Additional contact information
Anatoly Lisnianski: The Israel Electric Corporation Ltd.
Chapter Chapter 6 in Recent Advances in System Reliability, 2012, pp 79-95 from Springer
Abstract:
Abstract During last years a specific approach called the universal generating function (UGF) technique has been widely applied to MSS reliability analysis. The UGF technique allows one to algebraically find the entire MSS performance distribution through the performance distributions of its elements. However, the main restriction of this powerful technique is that theoretically it may be only applied to random variables and, so, concerning MSS reliability, it operates with only steady-states performance distributions. In order to extend the UGF technique application to dynamic MSS reliability analysis the paper introduces a special transform for a discrete-states continuous-time Markov process that is called L Z -transform. The transform was mathematically defined, its main properties were studied, and numerical example illustrating its benefits for dynamic MSS reliability assessment is presented.
Keywords: Discrete-state continuous-time Markov process; Universal generating function; Multi-state system; Reliability (search for similar items in EconPapers)
Date: 2012
References: Add references at CitEc
Citations: View citations in EconPapers (7)
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
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:spr:ssrchp:978-1-4471-2207-4_6
Ordering information: This item can be ordered from
http://www.springer.com/9781447122074
DOI: 10.1007/978-1-4471-2207-4_6
Access Statistics for this chapter
More chapters in Springer Series in Reliability Engineering from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().