Modeling and inference for troubleshooting with interventions applied to a heavy truck auxiliary braking system

Pernestål, Anna; Nyberg, Mattias; Warnquist, Håkan

doi:10.1016/j.engappai.2011.02.018

Cited by 23 publications

(26 citation statements)

References 5 publications

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“…An extension of the framework so that remote troubleshooting can be supported is presented in [11,12]. Prototype implementations of the framework have been created for a hydraulic braking system [7,8], an engine temperature control system, [11], and a fuel injection system [12]. The users of the troubleshooter can either be in the workshop together with the vehicle (e.g.…”

Section: Troubleshooting Frameworkmentioning

confidence: 99%

“…For the case when the vehicle is in the workshop only, the framework is presented in part in [1,4,8] and in full in [7]. An extension of the framework so that remote troubleshooting can be supported is presented in [11,12].…”

Section: Troubleshooting Frameworkmentioning

confidence: 99%

“…The following sections will discuss the contributions made in all of these publications in some detail. The publications [6,8,9,11,12] constitute the most significant contributions and these are appended as Papers A-E in the second part of the thesis.…”

Section: Contributionsmentioning

confidence: 99%

“…These are two published conference papers [6,9], two published journal papers [8,11], and one submitted journal manuscript [12].…”

Section: Outline Of Thesismentioning

confidence: 99%

“…Depending on the implementation of the diagnoser, these probabilities are computed given a set of previous observations [11] or a previous probability distribution and a new event [1,4,7,8,12]. The diagnoser uses a diagnostic model that describes probabilistic dependencies between component faults and observations.…”

Section: Troubleshooting Frameworkmentioning

confidence: 99%

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Troubleshooting Trucks: Automated Planning and Diagnosis

Warnquist¹

2015

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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.This work has been supported Scania CV AB and FFI -Fordonsstrategisk Forskning och Innovation. iii iv AcknowledgmentsDenna avhandling är slutet på en lång och spännande resa som inte hade varit möjligt utan hjälp. Arbetet med avhandlingen har varit en del av ett forskningssamarbete mellan fordonstillverkaren Scania och institutionen för datavetenskap vid Linköpings universitet.I Linköping vill jag tacka mina handledare Professor Patrick Doherty och Dr Jonas Kvarnström. Patrick har gett mig god träning i och verktyg för hur jag muntligt presenterar min forskning. Jonas har varit ett otroligt stöd vid framtagandet av nya forskningsidéer och forskningsartiklar. Med nästan övermänsklig noggrannhet kan hans skarpa ögon upptäcka minsta oklarhet i snåriga ekvationer. Jag är tacksam för all kunskap han delat med sig av. Jag har lärt mig mycket om hur man skriver vetenskapliga artiklar. Stort tack till Anne Moe, koordinator för doktorander på institutionen för datavetenskap. Hon är en klippa och har gett mig ovärderlig hjälp i den praktiska planeringen av framläggningarna av denna avhandling. Jag vill även tacka alla doktorander på KPLAB för alla spännande lunchdiskussioner.På Scania vill jag först och främst tacka Dr Mattias Nyberg som varit initiativtagare till forskningsprojekten och som gett mig förstklassig handledning. Mattias har tillsammans med Dr Jonas Biteus varit med och utvecklat tillämpningen av min forskning på Scania. Jag vill även tacka alla andra idésprutor som bidragit, i synnerhet Hans Ivendal, Dr Tony Lindgren, Dr Martin Eineborg och övriga i min v arbetsgrupp på Scania. Jag vill även tacka min styrgrupp och alla de skickliga chefer stöttat mig under denna tid: Göran Johansson, Thor Brenden, Per Karlsson, Anna Pernestål, Björn Bertmar och Erik Malmkvist.D...

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