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Modeling and inference for troubleshooting with interventions applied to a heavy truck auxiliary braking system
Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
2012 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, Vol. 25, no 4, 705-719 p.Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier , 2012. Vol. 25, no 4, 705-719 p.
Keyword [en]
Automobile industry, Decision support systems, Fault diagnosis, Probabilistic models, Bayesian network
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-77725DOI: 10.1016/j.engappai.2011.02.018ISI: 000303552100005OAI: diva2:529459
Available from: 2012-05-30 Created: 2012-05-28 Last updated: 2015-09-23
In thesis
1. Troubleshooting Trucks: Automated Planning and Diagnosis
Open this publication in new window or tab >>Troubleshooting Trucks: Automated Planning and Diagnosis
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Felsökning av lastbilar : automatiserad planering och diagnos
Abstract [en]

This thesis considers computer-assisted troubleshooting of heavy vehicles such as trucks and buses. In this setting, the person that is troubleshooting a vehicle problem is assisted by a computer that is capable of listing possible faults that can explain the problem and gives recommendations of which actions to take in order to solve the problem such that the expected cost of restoring the vehicle is low. To achieve this, such a system must be capable of solving two problems: the diagnosis problem of finding which the possible faults are and the decision problem of deciding which action should be taken.

The diagnosis problem has been approached using Bayesian network models. Frameworks have been developed for the case when the vehicle is in the workshop only and for remote diagnosis when the vehicle is monitored during longer periods of time.

The decision problem has been solved by creating planners that select actions such that the expected cost of repairing the vehicle is minimized. New methods, algorithms, and models have been developed for improving the performance of the planner.

The theory developed has been evaluated on models of an auxiliary braking system, a fuel injection system, and an engine temperature control and monitoring system.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2015. 79 p.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1691
automated planning, diagnosis, troubleshooting, automotive systems, Bayesian networks, Markov decision-processes
National Category
Computer Systems
urn:nbn:se:liu:diva-119445 (URN)10.3384/diss.diva-119445 (DOI)978-91-7685-993-3 (print) (ISBN)
Public defence
2015-10-16, Visionen, Hus B, Campus Valla, Linköping, 13:15 (English)
Available from: 2015-09-23 Created: 2015-06-17 Last updated: 2015-10-12Bibliographically approved

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Pernestål, AnnaNyberg, MattiasWarnquist, Håkan
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