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FlexDx : A Reconfigurable Diagnosis Framework
2010 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, Vol. 23, no 8, 1303-1313Article in journal (Refereed) Published
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.

Keyword [en]
Reconfigurable diagnosis framework, Diagnosing dynamical systems, Test reconfiguration, Test selection, Test initialization
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-59945 (URN)10.1016/j.engappai.2010.01.004 (DOI)000284297600007 (ISI)oai:DiVA.org:liu-59945 (OAI)diva2:354330 (DiVA)
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Available from2010-09-30 Created:2010-09-30 Last updated:2013-07-23

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Krysander, MattiasHeintz, FredrikRoll, JacobFrisk, Erik
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Vehicular SystemsThe Institute of TechnologyKPLAB - Knowledge Processing LabAutomatic Control
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Engineering applications of artificial intelligence
Control Engineering

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