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Guided Integrated Remote and Workshop Troubleshooting of Heavy Trucks
Scania CV AB, Södertälje, Sweden.
Scania CV AB, Södertälje, Sweden.
Scania CV AB, Södertälje, Sweden.
2014 (English)In: International Journal of Commercial Vehicles, ISSN 1946-391X (print), 1946-3928 (online), Vol. 7, no 1, 25-36 p.Article in journal (Refereed) Published
Abstract [en]

When a truck or bus suffers from a breakdown it is important that the vehicle comes back on the road as soon as possible. In this paper we present a prototype diagnostic decision support system capable of automatically identifying possible causes of a failure and propose recommended actions on how to get the vehicle back on the road as cost efficiently as possible.

This troubleshooting system is novel in the way it integrates the remote diagnosis with the workshop diagnosis when providing recommendations. To achieve this integration, a novel planning algorithm has been developed that enables the troubleshooting system to guide the different users (driver, help-desk operator, and mechanic) through the entire troubleshooting process.

In this paper we formulate the problem of integrated remote and workshop troubleshooting and present a working prototype that has been implemented to demonstrate all parts of the troubleshooting system.

Place, publisher, year, edition, pages
Warrendale, PA, USA: SAE International , 2014. Vol. 7, no 1, 25-36 p.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-121498DOI: 10.4271/2014-01-0284OAI: oai:DiVA.org:liu-121498DiVA: diva2:855936
Available from: 2015-09-22 Created: 2015-09-22 Last updated: 2015-10-05Bibliographically 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 (print) (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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