LiU Electronic Press
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Author:
Heintz, Fredrik (Linköping University, Department of Computer and Information Science) (Linköping University, The Institute of Technology)
Krysander, Mattias (Linköping University, Department of Electrical Engineering, Vehicular Systems) (Linköping University, The Institute of Technology)
Roll, Jacob (Linköping University, Department of Electrical Engineering, Automatic Control) (Linköping University, The Institute of Technology)
Frisk, Erik (Linköping University, Department of Electrical Engineering, Vehicular Systems) (Linköping University, The Institute of Technology)
Title:
FlexDx: A Reconfigurable Diagnosis Framework
Department:
Linköping University, Department of Electrical Engineering, Vehicular Systems
Linköping University, Department of Computer and Information Science
Linköping University, Department of Electrical Engineering, Automatic Control
Linköping University, The Institute of Technology
Publication type:
Conference paper (Refereed)
Language:
English
In:
Proceedings of the 19th International Workshop on Principles of Diagnosis (DX)
Conference:
19th International Workshop on Principles of Diagnosis, Blue Mountains NSW, Australia, September 2008
Year of publ.:
2008
URI:
urn:nbn:se:liu:diva-42835
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-42835
Local ID:
69185
Subject category:
Computer Science
Control Engineering
SVEP category:
Computer science
Keywords(en) :
FlexDX, Diagnosis, Isolation performance, DyKnow, Framework, System
Abstract(en) :

Detecting and isolating multiple faults is a computationally intense task which typically consists of computing a set of tests, and then computing the diagnoses based on the test results. This paper describes FlexDx, a reconfigurable diagnosis framework which reduces the computational burden by only running the tests that are currently needed. The method selects tests such that the isolation performance of the diagnostic system is maintained. Special attention is given to the practical issues introduced by a reconfigurable diagnosis framework such as FlexDx. For example, tests are added and removed dynamically, tests are partially performed on historic data, and synchronous and asynchronous processing are combined. To handle these issues FlexDx uses DyKnow, a stream-based knowledge processing middleware framework. The approach is exemplified on a relatively small dynamical system, which still illustrates the computational gain with the proposed approach.

Available from:
2009-10-10
Created:
2009-10-10
Last updated:
2013-02-20
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84 hits