A context tree method for multistage fault detection and isolation with applications to commercial video broadcasting systems

Pierre Brice and Wei Jiang

IISE Transactions, 2009, vol. 41, issue 9, 776-789

Abstract: Many systems have distributed functionalities among several autonomous components. Such complex systems can generally be described by finite state machines whose behavior is often non-linear and context-dependent. This paper proposes a generic system model based on context trees to predict system behavior for the purpose of fault detection and isolation in a multistage serial system. The approach starts with learning multistage model structures by capturing the expected statistical distribution of the input/output at different stages. The estimated model is then employed to detect departures by comparing the contexts of the new system output with a set of optimal contexts for each stage using the Kullback–Leibler divergence measure. Problems can then be isolated to the stage that contributes the most to these differences. The methodology is demonstrated by an application in commercial video broadcasting systems.

Date: 2009
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/07408170802323018 (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:41:y:2009:i:9:p:776-789

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

DOI: 10.1080/07408170802323018

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 ().