A Modeling Framework for Troubleshooting Automotive Systems

This paper presents a novel framework for modeling the troubleshooting process for automotive systems such as trucks and buses. We describe how a diagnostic model of the troubleshooting process can be created using event-driven nonstationary dynamic Bayesian networks. Exact inference in such a model is in general not practically possible. Therefore we evaluate different approximate methods for inference based on the BoyenKoller algorithm. We identify relevant model classes that have particular structure such that inference can be made with linear time complexity using a novel inference algorithm called the Quickscore Variant. We show how the algorithm can be applied for inference when only a single fault is assumed and when multiple faults are possible. We also show another inference method that can be used when multiple faults are possible but a single fault is most likely. We also show how models created using expert knowledge can be tuned using statistical data. The proposed learning mechanism can use data that is collected from a heterogeneous fleet of modular vehicles that can consist of different components. The proposed framework is evaluated both theoretically and experimentally on an application example of a fuel injection system.