Reliability Analysis of Multi-State Engine Units Utilizing Time-Domain Response Data

Yongfeng Fang (), Wenliang Tao () and Kong Fah Tee ()
Additional contact information
Yongfeng Fang: School of Electronic & Mechanical Engineering, Guizhou University of Science Technology, Bijie551700, China
Wenliang Tao: School of Electronic & Mechanical Engineering, Guizhou University of Science Technology, Bijie551700, China
Kong Fah Tee: Department of Engineering Science, University of Greenwich, KentME44TB, United Kingdom

Journal of Systems Science and Information, 2016, vol. 4, issue 4, 354-364

Abstract: A novel reliability-based approach has been developed for multi-state engine systems. Firstly, the output power of the engine is discretized and modeled as a discrete-state continuous-time Markov random process. Secondly, the multi-state Markov model is established. According to the observed data, the transition intensity is determined. Thirdly, the proposed method is extended to compute the forced outage rate and the expected engine capacity deficiency based on time response. The proposed method can therefore be used for forecasting and monitoring the reliability of the multi-state engine utilizing time-domain response data. It is illustrated that the proposed method is practicable, feasible and gives reasonable prediction which conforms to the engineering practice.

Keywords: multi-state; engine system; Markov; reliability; time response (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.21078/JSSI-2016-354-11 (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:bpj:jossai:v:4:y:2016:i:4:p:354-364:n:6

DOI: 10.21078/JSSI-2016-354-11

Access Statistics for this article

Journal of Systems Science and Information is currently edited by Shouyang Wang

More articles in Journal of Systems Science and Information from De Gruyter
Bibliographic data for series maintained by Peter Golla ().