Conference article

Parameter Estimation Methods for Fault Diagnosis using Modelica and FMI

Ahmad Alsaab
ESI UK, UK

Morgan Cameron
ESI Group, France

Colin Hough
ESI UK, UK

Purna Musunuru
ESI US R&D, USA

Download articlehttps://doi.org/10.3384/ecp20169179

Published in: Proceedings of the American Modelica Conference 2020, Boulder, Colorado, USA, March 23-25, 2020

Linköping Electronic Conference Proceedings 169:19, s. 179-185

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Published: 2020-11-03

ISBN: 978-91-7929-900-2

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

Abstract

We compare a number of different methods for estimating model parameters based on external stimuli. We examine the trade-offs between the different methodologies with respect to the modelling effort necessary to implement them and the granularity of the estimation obtained. In implementing these methods, we utilize Modelica and FMI. As an application we show how these methods can be combined with component fault modes to provide effective real-time estimates of the health of a physical asset based on thermal sensor data. In particular, we contrast the effectiveness of the different estimators in predicting the degree and location of fault.

Keywords

Parameter estimation, parameter tuning, fault diagnosis, Modelica, FMI

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