1 © 1998 hrl laboratories, llc. all rights reserved evaluation of bayesian networks used for...

1© 1998 HRL Laboratories, LLC. All Rights Reserved

Evaluation of Bayesian NetworksUsed for Diagnostics[1]

K. Wojtek Przytula: HRL Laboratories

Denver Dash: University of Pittsburgh

Don Thompson: Pepperdine University


DIAGNOSIS - MODEL BASED APPROACH

DOMAIN MODELAPPLICATION DOMAIN

DIAGNOSIS SUPPORT TOOL

QUERY

DECISION

TROUBLESHOOTER

AnnumciatorPanel

Fuel Press FPS

Air Box P TBS

Fuel Temp FTS

Air Temp ATS

Crankcase CCP

Oil Temp OTS

Coolant P 1

Coolant P 2

Coolant P 3

Oil P PresOPS

PowerWiring

HarnessEM 2000

Power Supply

Comm Interface

& Fault Data

EM 2000Wiring

Harness

FuelPump

PrimaryFilter

SensorWiring

Harness

Fuel Injectors9 - 16

LeftInjectorHarness

ECM Sender

Crankshaft Spd SRS

Crankshaft Pos TRS

ECM ReceiverRight

InjectorHarness

Fuel Injectors1 - 8

PerformanceSensors

Protective System

ABCD 74 VDC 24 VDC

SuctionStrainer

Thermo Mod AMOT Valve

FuelTank

30 psiBypass

Fuel Preheater

120 PSIRelief Valve

SecondaryFuel Filters

SensorComponent

Wiring Harness

Pipe

Wiring

FuelSystem

40 psi Relief Valve

Cold Plate

Fuel Pressure

To Fuel Tank


Bayesian Network Diagnostics

•Bayesian Networks as models for computerized diagnostic assistants

•Model evaluation has not been addressed

•Model quality determines diagnosis quality •Evaluation provides a basis for model performance estimation


GRAPHICAL MODEL FOR DIAGNOSIS (GRAPH AND PROBABILITY THEORY)

GRAPH (structure):

• Two Fault Nodes: F1, F2.

• Three Observation Nodes – e.g. symptoms and tests.

• Causal Links

PROBABILITIES (parameters)

• Prior Probabilities of Faults

• Conditional Probabilities of Observations given Faults

• The Model constitutes a joint probability distribution over the nodes.• It is obtained from data or knowledge or both.


Bayesian Network for Example of Car Diagnostics


Bayesian Network Evaluation]

Using Inference, Monte Carlo Simulation, & Visualization Techniques

–Step 1–Set Defective Component–Execute Forward Inference

–Step 2–Sample Observation States–Execute Reverse Inference


Forward Inference


Reverse Inference


Identification of Critical Elements Responsible for Incorrect Diagnosis

–Components with weak observations that cannot be diagnosed convincingly

–Strongly coupled components that implicate each other, so they cannot be effectively separated in diagnosis

–Components whose failures are misinterpreted as failures of other components

Evaluation Conclusions


Fuel Level Battery Starter CableFuel PumpFuel Filter Induction Coil

Sample Graph for Car Diagnosis Bayesian Network Model[1]


2-D Matrix for Car Diagnosis Bayesian Network Model1]

Starter

Fuel Level

Battery

Cable

Fuel Pump

Fuel Filter

Induction Coil

Prior Probabilities

True Defect

IMPLICATED

FAULT


3-D Matrix for Car Diagnosis Bayesian Network Model 1]

Prior Probabilities

Starter

Fuel Level BatteryCable

True Defect

IMPLICATED

FAULTFuel Pump

Fuel FilterInduction Coil

Starter

Fuel Level

Battery

Cable

Fuel Pump

Fuel Filter

Induction Coil


3-D Matrix for Bayesian NetworkModel for the Large Network [1]


Results and Conclusions [1]

RESULTS

• METHOD AND ALGORITHMS FOR ANALYSIS OF BAYESIAN NETWORKS FOR DIAGNOSTICS

• SOFTWARE PACKAGE FOR COMPUTATION AND DISPLAY OF THE ANALYSIS RESULTS

CONCLUSIONS

• THE RESULTS CAN BE USED AS A GUIDE IN TESTING OF THE MODEL

• THE METHOD CAN BE USED IN DESIGN OF SYSTEMS FOR DIAGNOSIBILITY

• THE METHOD IS APPLICABLE NOT ONLY TO DIAGNOSTICS

BUT TO GENERAL CLASS OF DECISION SUPPORT PROBLEMS

1 © 1998 hrl laboratories, llc. all rights reserved evaluation of bayesian networks used for...

Documents

rights reserved results

hrl laboratories denver

forward inference slide

reverse inference slide

model quality

diagnosis graph

graphical model

pepperdine university