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A Modeling Framework for Troubleshooting Automotive Systems
Linköping University, Department of Computer and Information Science. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-5500-8494
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
2016 (English)In: Applied Artificial Intelligence, ISSN 0883-9514, E-ISSN 1087-6545, Vol. 30, no 3, 257-296 p.Article in journal (Refereed) Published
Abstract [en]

This article presents a novel framework for modeling the troubleshooting process for automotive systems such as trucks and buses. We describe how a diagnostic model of the troubleshooting process can be created using event-driven, nonstationary, dynamic Bayesian networks. Exact inference in such a model is in general not practically possible. Therefore, we evaluate different approximate methods for inference based on the Boyen–Koller algorithm. We identify relevant model classes that have particular structure such that inference can be made with linear time complexity. We also show how models created using expert knowledge can be tuned using statistical data. The proposed learning mechanism can use data that is collected from a heterogeneous fleet of modular vehicles that can consist of different components. The proposed framework is evaluated both theoretically and experimentally on an application example of a fuel injection system.

Place, publisher, year, edition, pages
Taylor & Francis, 2016. Vol. 30, no 3, 257-296 p.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-121499DOI: 10.1080/08839514.2016.1156955ISI: 000374866700005OAI: oai:DiVA.org:liu-121499DiVA: diva2:855938
Projects
ELLIITCADICS
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Note

The published article is a shorter version than the version in manuscript form. The status of this article was earlier Manuscript.

Funding agencies: Scania CV AB; FFI - Strategic Vehicle Research and Innovation; Excellence Center at Linkoping and Lund in Information Technology (ELLIIT); Research Council (VR) Linnaeus Center CADICS

Available from: 2015-09-22 Created: 2015-09-22 Last updated: 2017-04-14Bibliographically approved
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.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1691
Keyword
automated planning, diagnosis, troubleshooting, automotive systems, Bayesian networks, Markov decision-processes
National Category
Computer Systems
Identifiers
urn:nbn:se:liu:diva-119445 (URN)10.3384/diss.diva-119445 (DOI)978-91-7685-993-3 (ISBN)
Public defence
2015-10-16, Visionen, Hus B, Campus Valla, Linköping, 13:15 (English)
Opponent
Supervisors
Funder
VINNOVA, 2010-02864
Available from: 2015-09-23 Created: 2015-06-17 Last updated: 2015-10-12Bibliographically approved

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Warnquist, HåkanKvarnström, JonasDoherty, Patrick
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