Modelling and hierarchical diagnosis of timed discrete-event systems
Gernot Schullerus,
Peerasan Supavatanakul,
Volker Krebs and
Jan Lunze
Mathematical and Computer Modelling of Dynamical Systems, 2006, vol. 12, issue 6, 519-542
Abstract:
A new diagnostic method for hierarchically structured discrete-event systems is presented. The efficiency of this method results from the fact that the complexity of the diagnostic task is reduced by first detecting a faulty component using a coarse process model on a high level of abstraction, and subsequently refining the result by investigating the faulty component with the help of a detailed component model in order to identify the fault with sufficient precision. On both abstraction levels, the method uses a timed discrete-event model of the appropriate part of the system. On the higher abstraction level a timed event graph is used that describes how the temporal distance of the events is changed by component faults. On the lower level of abstraction, timed automata are used to cope with the non-determinism of the event sequence generated by the faulty and faultless components. The approach is illustrated by the diagnosis of a batch process.
Date: 2006
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://hdl.handle.net/10.1080/13873950500241479 (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:nmcmxx:v:12:y:2006:i:6:p:519-542
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/NMCM20
DOI: 10.1080/13873950500241479
Access Statistics for this article
Mathematical and Computer Modelling of Dynamical Systems is currently edited by I. Troch
More articles in Mathematical and Computer Modelling of Dynamical Systems from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().