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 2 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 Automobile Troubleshooting using Bayesian Network 3 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 Automobile Troubleshooting using Bayesian Network 4 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 © 2006 IARC Automobile Troubleshooting using Bayesian Network 5 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 © 2006 IARC Automobile Troubleshooting using Bayesian Network 6 Probability Table © 2006 IARC Automobile Troubleshooting using Bayesian Network 7 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 © 2006 IARC Automobile Troubleshooting using Bayesian Network 8 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 © 2006 IARC Automobile Troubleshooting using Bayesian Network 9 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 Automobile Troubleshooting using Bayesian Network 10 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 © 2006 IARC Automobile Troubleshooting using Bayesian Network 11 Case study 1 --- A Model for Transmission Sensor © 2006 IARC Automobile Troubleshooting using Bayesian Network 12 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. © 2006 IARC Automobile Troubleshooting using Bayesian Network 13 Case study 2 --- A Model for ABS Main model: © 2006 IARC Automobile Troubleshooting using Bayesian Network 14 Case study 2 --- A Model for ABS Sub-model for acceleration sensor : © 2006 IARC Automobile Troubleshooting using Bayesian Network 15 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 16 Demonstration © 2006 IARC Automobile Troubleshooting using Bayesian Network 17 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 © 2006 IARC Automobile Troubleshooting using Bayesian Network 18 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 © 2006 IARC Automobile Troubleshooting using Bayesian Network 19