Automobile Troubleshooting
Based on Bayesian Network
Dr Yingping Huang
Electrical Test for Advanced Architecture
Yingping.huang@warwick.ac.uk
© 2006 IARC
Agenda
¾
Background
¾
Methodology
¾
Introduction to Bayesian Network
¾
Case studies
¾
Demonstrations
¾
Conclusions
© 2006 IARC
Automobile Troubleshooting using Bayesian Network
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Limitations of Existing Diagnostic Tools
¾
Simple yes-or-no judgment with rigid
structure of guided diagnostics
¾
Single- fault-oriented
¾
No considerations on the cost of the
actions
© 2006 IARC
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Objective
An expert-imitated analysis engine for automobile
diagnostics:
‰
‰
‰
Guide diagnostics in probabilistic basis
Diagnose multiple DTCs simultaneously
Consider cost and time for the repair
Diagnostic Bayesian Network
WDS
© 2006 IARC
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Methodology
Bayesian Belief Network
+
Multi-criteria Decision Making
PART II
FTA
FMEA
SPA
DTCs
Or
Failure
Symptoms
.
.
.
Statistic data
Historic analysis
Bayesian Learning
Bayesian
Belief
Network
1
2
.
.
.
3
Cost of repair
Time for repair
Multicriteria
decision
making
2
1
.
.
.
3
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What is Bayesian Belief Network (BBN)
¾
Probability-based graphic model, reflecting the
states of a system, indicating how those states
are related by probabilities
¾
Consisting of nodes, directed link and
probability table, also called directed acyclic
graph
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Probability Table
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Propagation of Bayesian Network
¾
Basically, inspired from Bayesian Theorem
P(eλ) P(e / λ) ∗ P(λ)
=
P(λ / e) =
P(e)
P(e)
¾
BN propagation involves calculating the joint
probability, which is probabilities of all combined
states for all nodes
¾
Three types of conditional independence greatly
simplifies the calculation of joint probability
¾
Junction tree method compiles the diagram into a
junction tree of cliques to localise computation to
those nodes that are directly related
λ
e
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Suitability and Advantage
¾
¾
¾
¾
Knowledge-based applications
Model and reason about uncertainty
Network is understandable, clear indication of
causal relationship
Resistant to the modelling error and to data
input errors
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Multi-criteria Decision Making Algorithm
Cost of repair
Time for repair
1
2
Probability
.
.
.
3
Multicriteria
decision
making
2
1
.
.
.
3
n
U (a) = ∑[Wi ∗ Ci (a)]
i =1
U(a) --- overall utility score of an action a
n --- number of the criteria
Ci(a) --- value of the ith criteria of the action a
Wi --- utility weighting for the ith criteria
© 2006 IARC
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Knowledge Collection and Analysis
To build up a diagnostic model, we need:
¾
Model Structure
‰
‰
‰
Specific diagnostic specifications PART II
Failure mode effect analysis (FMEA)
Fault tree analysis (FTA)
¾ Probability
‰
‰
Bayesian Learning by using statistic data such as
warranty databases
Experience from the knowledge engineer
¾ Cost and time for repair
‰
Analytic Warranty System
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Case study 1 --- A Model for Transmission Sensor
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Case study 2 --- A Model for ABS
Object oriented network structure:
¾
Model boundary was defined as a single ECU
¾
An object oriented model is consists of one main model
and a number of sub-models.
¾
Main model gives an overview of the diagnostic model
and indicates the links between the sub-models.
¾
Sub-models are constructed for individual components or
component clusters grouped in terms of their
functionality, and can be reused as a class.
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Case study 2 --- A Model for ABS
Main model:
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Case study 2 --- A Model for ABS
Sub-model for acceleration sensor :
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The Prototype of Diagnostic Engine
¾
Able to give diagnostics steps based on
overall utility score of the multiple criteria
including probability, cost, time and risk
¾
System development using C++
programming
¾
Huigin engine (API) is employed for the
Bayesian Network propagation
© 2006 IARC
Automobile Troubleshooting using Bayesian Network
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Demonstration
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New Concept for Future Diagnostic Tool
¾
Guide diagnostics on a probabilistic basis
¾
Multiple DTCs-oriented diagnostic strategy
¾
Consideration of cost information (money, time)
of troubleshooting actions
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Next Steps
Completion of case studies
‰
Evaluate the method in real sceneries Contributions of case studies
or to adopt methods and evaluate
Define and implement any Technology transfer
mechanisms
Final report on application of technique
‰
Available to research partners
Develop recommendations for further research
‰
Definition of new areas where this technique could be applied
‰
Extending current research
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