Prediction of the failure interval with maximum power based on the remaining useful life distribution

Junbo Son, Qiang Zhou, Shiyu Zhou and Mutasim Salman

IISE Transactions, 2015, vol. 47, issue 10, 1072-1087

Abstract: Prognosis of the Remaining Useful Life (RUL) of a unit or system plays an important role in system reliability analysis and maintenance decision making. One key aspect of the RUL prognosis is the construction of the best prediction interval for failure occurrence. The interval should have a reasonable length and yield the best prediction power. In current practice, the center-based interval and traditional confidence interval are widely used. Although both are easy to construct, they do not provide the best prediction performance. In this article, we propose a new scheme, the Maximum Power Interval (MPI), for estimating the interval with maximum prediction power. The MPI guarantees the best prediction power under a given interval length. Some technical challenges involved in the MPI method were resolved using the maximum entropy principle and truncation method. A numerical simulation study confirmed that the MPI has better prediction power than other prediction intervals. A case study using a real industry data set was conducted to illustrate the capability of the MPI method.

Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/0740817X.2014.999899 (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:uiiexx:v:47:y:2015:i:10:p:1072-1087

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/uiie20

DOI: 10.1080/0740817X.2014.999899

Access Statistics for this article

IISE Transactions is currently edited by Jianjun Shi

More articles in IISE Transactions from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().