Event log modeling and analysis for system failure prediction

Yuan Yuan, Shiyu Zhou, Crispian Sievenpiper, Kamal Mannar and Yibin Zheng

IISE Transactions, 2011, vol. 43, issue 9, 647-660

Abstract: Event logs, commonly available in modern mechatronic systems, contain rich information on the operating status and working conditions of the system. This article proposes a method to build a statistical model using event logs for system failure prediction. To achieve the best prediction performance, prescreening and statistical variable selection are adopted to select the best set of predictor events, coded as covariates in the statistical model. In-depth discussion of the prediction power of the model in terms of false alarm and misdetection probability is presented. Using a real-world example, the effectiveness of the proposed method is further confirmed.

Date: 2011
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/0740817X.2010.546385 (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:43:y:2011:i:9:p:647-660

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

DOI: 10.1080/0740817X.2010.546385

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 ().