Evaluation of Bayesian Networks Used for Diagnostics[1] K. Wojtek Przytula: HRL Laboratories Denver Dash: University of Pittsburgh Don Thompson: Pepperdine University

DIAGNOSIS SUPPORT TOOL DIAGNOSIS - MODEL BASED APPROACH APPLICATION DOMAIN DOMAIN MODEL TROUBLESHOOTER DIAGNOSIS SUPPORT TOOL QUERY DECISION

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

Evaluation Conclusions 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

Sample Graph for Car Diagnosis Bayesian Network Model[1] Fuel Level Fuel Filter Battery Fuel Pump Starter Induction Coil Cable

2-D Matrix for Car Diagnosis Bayesian Network Model1] Starter Fuel Level Battery Cable Fuel Pump Fuel Filter Induction Coil I M P L C A T E D F U True Defect Prior Probabilities

3-D Matrix for Car Diagnosis Bayesian Network Model 1] Prior Probabilities I M P L C A T E D F U Starter Fuel Level Battery Cable Fuel Pump Starter Fuel Level Fuel Filter Battery Cable Induction Coil Fuel Pump Fuel Filter Induction Coil True Defect

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

Results and Conclusions [1] 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