A Probabilistic Vehicle Diagnostic System Using Multiple Models

Matthew L. Schwall, J. Christian Gerdes, Bernard Bäker, and Thomas Forchert

In addition to being accurate, it is important that diagnostic systems for use in automobiles also have low development and hardware costs. Model-based methods have shown promise at reducing hardware costs since they use analytical redundancy to reduce physical redundancy. In addition to requiring no extra sensors, the diagnostic system presented in this paper also allows for high accuracy and low development costs by using information from multiple simple models. This is made possible by the use of a Bayesian network to process model residuals. A hybrid, dynamic Bayesian network is used to model the temporal behavior of the faults and determine fault probabilities. A prototype of the system has been implemented and tested on a Mercedes-Benz E320 sedan. This paper describes the prototype system and presents results demonstrating the system’s advantages over traditional residual threshold techniques.


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