Ryan Magargle
ANSYS Inc., USA
Lee Johnson
ANSYS Inc., USA
Padmesh Mandloi
ANSYS Inc., USA
Peyman Davoudabadi
ANSYS Inc., USA
Omkar Kesarkar
ANSYS Inc., USA
Sivasubramani Krishnaswamy
ANSYS Inc., USA
John Batteh
Modelon Inc., USA
Anand Pitchaikani
Modelon Inc., USA
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http://dx.doi.org/10.3384/ecp1713235Published in: Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, May 15-17, 2017
Linköping Electronic Conference Proceedings 132:3, p. 35-46
Published: 2017-07-04
ISBN: 978-91-7685-575-1
ISSN: 1650-3686 (print), 1650-3740 (online)
This paper describes a model-driven approach to support heat monitoring and predictive maintenance of an automotive braking system. This approach includes the creation of a simulation-based digital twin that combines models and different modeling formalisms into an integrated model of the braking system that can be used for monitoring, diagnostics, and prognostics. The paper provides an overview of the basic models including Modelica models, reduced order models for various key components of the system model, and controls and sensor models. The simulation results include both baseline results for the system and the results of injecting failures into the system for monitoring and predictive maintenance.
digital twin; electronics;
Electromagnetics; hydraulics; pneumatics; braking system; automotive; FEA;
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