LiU Electronic Press
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Author:
Pernestål, Anna (Linköping University, Department of Electrical Engineering) (Linköping University, The Institute of Technology)
Nyberg, Mattias (Linköping University, Department of Electrical Engineering, Vehicular Systems) (Linköping University, The Institute of Technology)
Warnquist, Håkan (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab) (Linköping University, The Institute of Technology)
Title:
Modeling and inference for troubleshooting with interventions applied to a heavy truck auxiliary braking system
Department:
Linköping University, Department of Electrical Engineering, Vehicular Systems
Linköping University, Department of Electrical Engineering
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab
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:
25
Issue:
4
Pages:
705-719
Year of publ.:
2012
URI:
urn:nbn:se:liu:diva-77725
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-77725
ISI:
000303552100005
Subject category:
Engineering and Technology
SVEP category:
TECHNOLOGY
Keywords(en) :
Automobile industry, Decision support systems, Fault diagnosis, Probabilistic models, Bayesian network
Abstract(en) :

Computer assisted troubleshooting with external interventions is considered. The work is motivated by the task of repairing an automotive vehicle at lowest possible expected cost. The main contribution is a decision theoretic troubleshooting system that is developed to handle external interventions. In particular, practical issues in modeling for troubleshooting are discussed, the troubleshooting system is described, and a method for the efficient probability computations is developed. The troubleshooting systems consists of two parts; a planner that relies on AO* search and a diagnoser that utilizes Bayesian networks (BN). The work is based on a case study of an auxiliary braking system of a modern truck. Two main challenges in troubleshooting automotive vehicles are the need for disassembling the vehicle during troubleshooting to access parts to repair, and the difficulty to verify that the vehicle is fault free. These facts lead to that probabilities for faults and for future observations must be computed for a system that has been subject to external interventions that cause changes in the dependency structure. The probability computations are further complicated due to the mixture of instantaneous and non-instantaneous dependencies. To compute the probabilities, we develop a method based on an algorithm, updateBN, that updates a static BN to account for the external interventions.

Available from:
2012-05-30
Created:
2012-05-28
Last updated:
2012-05-30
Statistics:
66 hits