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Benchmark of Probabilistic Methods for Fault Diagnosis
KTH, School of Electrical Engineering (EES), Automatic Control.
2007 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

To be able to do the correct action when a fault is detected, the fault isolation part must be precise and run in real time during operation of the process. In many cases can it be difficult to decide exactly where the fault is localized. In those cases, the isolation algorithm must rank the faults according to their probability to be the cause to the behavior.

The masters thesis project aims at probabilistic methods and algorithms for fault isolation in embedded systems. Different kind of Bayesian Networks have been compared in this report and the comparison has been done on a literature defined “benchmark system”. Those Bayesian network models which have been implemented for fault isolation are:

1. Manually (on the basis of physical representations)

2. Two-layer structure

  • continuous signals
  • discreet signals

3. Via temporal causal graph (dynamical network)

The algorithms should be compared in the following areas: computational complexity, isolation performance and degree of difficulty to construct the network on the basis of data.

The evaluated algorithms showed good results. Even though the system data which have been used in the Bayesian Networks are not very accurate in the first place, it manage to give a fairly precise isolation of the faults. The continuous Bayesian Network manage to show a good isolation performance for different type of faults and the Dynamic Bayesian Network found most of the faults even for a rather complex network.

Abstract [sv]

Detta examensarbete handlar om sannolikhetsbaserade metoder för felisolering. När ett fel uppstår ombord på en Scania lastbil kan man upptäcka det. I bästa fall kan en viss komponent pekas ut som orsak, men ofta kommer man att ha ett antal komponenter som kan vara orsaken. I många fall är det dock svårt att hitta var exakta felet finns. För att hantera dessa situationer vill man använda metoder för att beräkna sannolikheten att olika komponenter är trasiga.

För att beräkna sannolikheten kan man använda en probabilistisk model, dvs. Bayesianska nätverk. I detta arbete har olika metoder för att skapa Bayesianska nätverk jämförts. Jämförelsen görs på ett litteratur väl definierat benchmark problem: diagnosar en två-tank system. De typer av Bayesiansk nätverks modeller som har implementerats för felisolering är:

1. Manuellt (ut ifrån fysikalisk modell)

2. Två-lagers struktur

  • kontinuerliga signaler
  • diskreta signaler

3. Via Bindningsgrafer (dynamiskt nätverk)

Problemen som undersöktes var bland annat svårighet att bygga nätverket utifrån data, beräkningskomplexitet samt isolerings prestanda. En jämförelse mellan de Bayesianska metoderna för felisolering och samt dem befintliga standardmetoder har även gjorts.

De undersökta algoritmerna visade goda resultat. Trots bristen på data, visade algoritmerna lovande resultat. Det Två-lagers Bayesianska nätverket visade en bra isoleringsprestanda på olika komponent fel och det Dynamiska Bayesianska nätverket upptäckte de flesta fel trots att det var ett ganska complext nätverk.

Place, publisher, year, edition, pages
2007. , 73 p.
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-106235OAI: oai:DiVA.org:kth-106235DiVA: diva2:573397
Subject / course
Automatic Control
Educational program
Master of Science in Engineering
Uppsok
Technology
Examiners
Available from: 2012-12-03 Created: 2012-11-30 Last updated: 2012-12-03Bibliographically approved

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