Conference article

Using Fault Augmented Modelica Models for Diagnostics

Raj Minhas
Palo Alto Research Center, Palo Alto, CA, USA

Johan de Kleer
Palo Alto Research Center, Palo Alto, CA, USA

Ion Matei
Palo Alto Research Center, Palo Alto, CA, USA

Bhaskar Saha
Palo Alto Research Center, Palo Alto, CA, USA

Bill Janssen
Palo Alto Research Center, Palo Alto, CA, USA

Daniel G. Bobrow
Palo Alto Research Center, Palo Alto, CA, USA

Tolga Kurtoglu
Palo Alto Research Center, Palo Alto, CA, USA

Download articlehttp://dx.doi.org/10.3384/ecp14096437

Published in: Proceedings of the 10th International Modelica Conference; March 10-12; 2014; Lund; Sweden

Linköping Electronic Conference Proceedings 96:46, p. 437-445

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Published: 2014-03-10

ISBN: 978-91-7519-380-9

ISSN: 1650-3686 (print), 1650-3740 (online)

Abstract

We propose a model-based diagnosis framework in which Modelica models of faulted behavior are used in combination with a Bayesian approach. The fault augmented models are automatically generated through a process developed as part of our Fault Augmented Model Extension (FAME) work. Fault diagnosis using a Bayesian approach is based on computing a set of probability density functions; a process that is usually intractable for any reasonably complex system.We use Approximate Bayesian Computation (ABC) to bound the numerical and computational complexity. The basic idea is to use fault augmented Modelica models to create probability distributions of possible outcomes and then compare those distributions against actual observations to perform parameter estimation. The detection of faults is treated as a model selection problem and the inference of their severity levels is treated as parameter estimation. The diagnostic precision of this approach is evaluated on a Modelica vehicle drive line model.

Keywords

Fault models; diagnosis; machine learning; model translation;bayesian methods

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