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Author:
Krysander, Mattias (Linköping University, Department of Electrical Engineering, Vehicular Systems) (Linköping University, The Institute of Technology)
Heintz, Fredrik (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab) (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, KPLAB - Knowledge Processing Lab
Linköping University, Department of Electrical Engineering, Automatic Control
Linköping University, The Institute of Technology
Publication type:
Article in journal (Refereed)
Language:
English
Publisher: Elsevier
Status:
Published
In:
Engineering applications of artificial intelligence(ISSN 0952-1976)
Volume:
23
Issue:
8
Pages:
1303-1313
Year of publ.:
2010
URI:
urn:nbn:se:liu:diva-59945
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-59945
ISI:
000284297600007
Subject category:
Control Engineering
SVEP category:
Automatic control
Keywords(en) :
Reconfigurable diagnosis framework, Diagnosing dynamical systems, Test reconfiguration, Test selection, Test initialization
Project:
CADICS
Abstract(en) :

Detecting and isolating multiple faults is a computationally expensive task. It 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 while retaining the isolation performance by only running a subset of all tests that is sufficient to find new conflicts. Tests in FlexDx are thresholded residuals used to indicate conflicts in the monitored system. Special attention is given to the issues introduced by a reconfigurable diagnosis framework. 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 has been implemented using DyKnow, a stream-based knowledge processing middleware framework. Concrete methods for each component in the FlexDx framework are presented. The complete approach is exemplified on a dynamic system which clearly illustrates the complexity of the problem and the computational gain of the proposed approach.

Available from:
2010-09-30
Created:
2010-09-30
Last updated:
2013-07-23
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