LiU Electronic Press
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Author:
Pernestål, Anna (Linköping University, Department of Electrical Engineering, Vehicular Systems) (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) (Linköping University, The Institute of Technology)
Title:
Modeling and Efficient Inference for Troubleshooting Automotive Systems
Department:
Linköping University, Department of Electrical Engineering, Vehicular Systems
Linköping University, Department of Computer and Information Science
Linköping University, The Institute of Technology
Publication type:
Report (Other academic)
Language:
English
Place of publ.: Linköping Publisher: Linköpings universitet
Series:
LiTH-ISY-R, ISSN 1400-3902; 2921
Year of publ.:
2009
URI:
urn:nbn:se:liu:diva-51928
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-51928
ISRN:
LiTH-ISY-R-2921
Subject category:
Engineering and Technology
SVEP category:
TECHNOLOGY
Abstract(en) :

We consider computer assisted troubleshooting of automotive vehicles, where the objective is to repair the vehicle at as low expected cost as possible.

The work has three main contributions: a troubleshooting method that applies to troubleshooting in real environments, the discussion on practical issues in modeling for troubleshooting, and the efficient probability computations.

The work is based on a case study of an auxiliary braking system of a modern truck.

We apply a decision theoretic approach, consisting of a planner and a diagnoser.

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 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:
2009-11-24
Created:
2009-11-24
Last updated:
2011-02-27
Statistics:
76 hits