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Modeling of Fuel InjectionSystem for Troubleshooting
KTH, School of Computer Science and Communication (CSC).
2013 (English)Independent thesis Basic level (degree of Bachelor), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

With the technology progressing further, making heavy duty vehicles more complex, more computerized, it

becomes necessary to update the troubleshooting process of such vehicles. From the vehicles´ computers,

diagnosis trouble codes can be extracted, informing the mechanic about the health of the vehicle. Using

those codes, together with physical observations, as input for a diagnosing software, the software can

give educated troubleshooting advice to the mechanic. Such diagnosing software requires a model of the

vehicle or one of its system, which mimics the behavior of the real one. If there would be a one-to-one

correspondence between observations and diagnosis, the model would be completely accurate. However,

no such one-to-one correspondence exists. This makes the system non-deterministic. therefore the model

has to be constructed using another approach. This master thesis presents a statistical model of a fuel

injection system called XPI. The XPI-system is modeled using a statistical model called a Bayesian

network which is a convenient way to model a non-deterministic system. The purpose of this model is

to be able to make diagnosis of the XPI-system, but also to ease the understanding of the dependency

between components and faults in the XPI-system. The model is also used to evaluate detectability and

isolability of faults in the XPI-system.

Abstract [sv]

Då teknologins utveckling gör tunga fordon mer komplexa, mer datorberoende, blir det nödvändigt att

modernisera felsökning av dessa fordon. Från fordonens datorer kan felkoder avläsas. Dessa felkoder meddelar

mekanikern om fordonets skick. Felkoderna, i kombination med fysiska observationer, kan användas

som indata till en diagnostiseringsmjukvara, som kan förse mekanikerna med kvalificerade felsökningsråd.

En sådan diagnostiseringsmjukvara kräver en modell av fordonet, eller ett delsystem av det, vilken

modellerar beteendet av det riktiga systemet. Om det skulle finnas en ett-till-ett mappning mellan observationer

och diagnoser, skulle modellen ha fullständig precision. Dessvärre finns det ingen sådan ett-till-ett

mappning. Modellen måste således konstrueras med en annan metod. Detta examensarbete presenterar

en statistisk modell av ett bränslesystem kallat XPI. Denna typ av statistiska modell kallas för ett Bayesianskt

nätverk, som är lämpligt att använda vid modellering av icke-deterministiska system. Syftet med

denna modell är att den ska kunna diagnostisera XPI-systemet samt underlätta förståelse för beroendet

mellan komponenter och fel. Modellen kan också användas för att utvärdera urskiljbarhet och isolerbarhet

hos fel i XPI-systemet.

Place, publisher, year, edition, pages
2013.
Series
Kandidatexjobb CSC, 13:105Trita-CSC-E, ISSN 1653-5715
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-137969OAI: oai:DiVA.org:kth-137969DiVA: diva2:679896
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2013-12-17 Created: 2013-12-17 Last updated: 2013-12-17Bibliographically approved

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