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Troubleshooting Trucks: Automated Planning and Diagnosis
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, Faculty of Science & Engineering. Scania CV AB.
2015 (English)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Felsökning av lastbilar : automatiserad planering och diagnos (Swedish)
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 [en]
automated planning, diagnosis, troubleshooting, automotive systems, Bayesian networks, Markov decision-processes
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:liu:diva-119445DOI: 10.3384/diss.diva-119445ISBN: 978-91-7685-993-3 (print)OAI: oai:DiVA.org:liu-119445DiVA: diva2:856040
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
List of papers
1. Iterative Bounding LAO*
Open this publication in new window or tab >>Iterative Bounding LAO*
2010 (English)In: ECAI 2010: 19th European Conference on Artificial Intelligence - Volume 215 Frontiers in Artificial Intelligence and Applications / [ed] Helder Coelho, Rudi Studer and Mike Wooldridge, IOS Press , 2010, 341-346 p.Conference paper, Published paper (Refereed)
Abstract [en]

Iterative Bounding LAO* is a new algorithm for epsilon- optimal probabilistic planning problems where an absorbing goal state should be reached at a minimum expected cost from a given initial state. The algorithm is based on the LAO* algorithm for finding optimal solutions in cyclic AND/OR graphs. The new algorithm uses two heuristics, one upper bound and one lower bound of the optimal cost. The search is guided by the lower bound as in LAO*, while the upper bound is used to prune search branches. The algorithm has a new mechanism for expanding search nodes, and while maintaining the error bounds, it may use weighted heuristics to reduce the size of the explored search space. In empirical tests on benchmark problems, Iterative Bounding LAO* expands fewer search nodes compared to state of the art RTDP variants that also use two-sided bounds.

Place, publisher, year, edition, pages
IOS Press, 2010
Series
Frontiers in Artificial Intelligence and Applications, ISSN 0922-6389 ; 215
National Category
Computer Science
Identifiers
urn:nbn:se:liu:diva-59883 (URN)10.3233/978-1-60750-606-5-341 (DOI)978-1-60750-605-8 (ISBN)978-1-60750-606-5 (ISBN)
Conference
The 19th European Conference on Artificial Intelligence (ECAI), August 16-20, Lisbon, Portugal
Available from: 2010-09-29 Created: 2010-09-29 Last updated: 2015-09-23Bibliographically approved
2. Modeling and inference for troubleshooting with interventions applied to a heavy truck auxiliary braking system
Open this publication in new window or tab >>Modeling and inference for troubleshooting with interventions applied to a heavy truck auxiliary braking system
2012 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, 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
Keyword
Automobile industry, Decision support systems, Fault diagnosis, Probabilistic models, Bayesian network
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-77725 (URN)10.1016/j.engappai.2011.02.018 (DOI)000303552100005 ()
Available from: 2012-05-30 Created: 2012-05-28 Last updated: 2017-12-07
3. Exploiting Fully Observable and Deterministic Structures in Goal POMDPs
Open this publication in new window or tab >>Exploiting Fully Observable and Deterministic Structures in Goal POMDPs
2013 (English)In: Proceedings of the 23rd International Conference on Automated Planning and Scheduling (ICAPS) / [ed] Daniel Borrajo, Subbarao Kambhampati, Angelo Oddi, Simone Fratini, AAAI Press, 2013, 242-250 p.Conference paper, Published paper (Refereed)
Abstract [en]

When parts of the states in a goal POMDP are fully observable and some actions are deterministic it is possibleto take advantage of these properties to efficiently generate approximate solutions. Actions that deterministically affect the fully observable component of the world state can be abstracted away and combined into macro actions, permitting a planner to converge more quickly. This processing can be separated from the main search procedure, allowing us to leverage existing POMDP solvers. Theoretical results show how a POMDP can be analyzed to identify the exploitable properties and formal guarantees are provided showing that the use of macro actions preserves solvability. The efficiency of the method is demonstrated with examples when used in combination with existing POMDP solvers.

Place, publisher, year, edition, pages
AAAI Press, 2013
National Category
Computer Science
Identifiers
urn:nbn:se:liu:diva-88637 (URN)978-1-57735-609-7 (ISBN)
Conference
23rd International Conference on Automated Planning and Scheduling (ICAPS 2013), 10-14 June 2013, Rom, Italy
Projects
ELLIITSHERPACUASCADICS
Available from: 2013-02-14 Created: 2013-02-14 Last updated: 2017-02-10Bibliographically approved
4. Guided Integrated Remote and Workshop Troubleshooting of Heavy Trucks
Open this publication in new window or tab >>Guided Integrated Remote and Workshop Troubleshooting of Heavy Trucks
2014 (English)In: International Journal of Commercial Vehicles, ISSN 1946-391X, 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
National Category
Computer Science
Identifiers
urn:nbn:se:liu:diva-121498 (URN)10.4271/2014-01-0284 (DOI)
Available from: 2015-09-22 Created: 2015-09-22 Last updated: 2015-10-05Bibliographically approved
5. A Modeling Framework for Troubleshooting Automotive Systems
Open this publication in new window or tab >>A Modeling Framework for Troubleshooting Automotive Systems
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
National Category
Computer Science
Identifiers
urn:nbn:se:liu:diva-121499 (URN)10.1080/08839514.2016.1156955 (DOI)000374866700005 ()
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: 2017-12-01Bibliographically approved

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