Article | Proceedings of the 10<sup>th</sup> International Modelica Conference; March 10-12; 2014; Lund; Sweden | Using Fault Augmented Modelica Models for Diagnostics
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Title:
Using Fault Augmented Modelica Models for Diagnostics
Author:
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
DOI:
10.3384/ecp14096437
Download:
Full text (pdf)
Year:
2014
Conference:
Proceedings of the 10th International Modelica Conference; March 10-12; 2014; Lund; Sweden
Issue:
096
Article no.:
046
Pages:
437-445
No. of pages:
9
Publication type:
Abstract and Fulltext
Published:
2014-03-10
ISBN:
978-91-7519-380-9
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press; Linköpings universitet


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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

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

Author:
Raj Minhas, Johan de Kleer, Ion Matei, Bhaskar Saha, Bill Janssen, Daniel G. Bobrow, Tolga Kurtoglu
Title:
Using Fault Augmented Modelica Models for Diagnostics
DOI:
http://dx.doi.org/10.3384/ecp14096437
References:

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

Author:
Raj Minhas, Johan de Kleer, Ion Matei, Bhaskar Saha, Bill Janssen, Daniel G. Bobrow, Tolga Kurtoglu
Title:
Using Fault Augmented Modelica Models for Diagnostics
DOI:
http://dx.doi.org/10.3384/ecp14096437
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