Article | Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, May 15-17, 2017 | A Simulation-Based Digital Twin for Model-Driven Health Monitoring and Predictive Maintenance of an Automotive Braking System
Göm menyn

Title:
A Simulation-Based Digital Twin for Model-Driven Health Monitoring and Predictive Maintenance of an Automotive Braking System
Author:
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
DOI:
10.3384/ecp1713235
Download:
Full text (pdf)
Year:
2017
Conference:
Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, May 15-17, 2017
Issue:
132
Article no.:
003
Pages:
35-46
No. of pages:
12
Publication type:
Abstract and Fulltext
Published:
2017-07-04
ISBN:
978-91-7685-575-1
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


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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.

Keywords: digital twin electronics electromagnetics hydraulics pneumatics braking system automotive FEA

Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, May 15-17, 2017

Author:
Ryan Magargle, Lee Johnson, Padmesh Mandloi, Peyman Davoudabadi, Omkar Kesarkar, Sivasubramani Krishnaswamy, John Batteh, Anand Pitchaikani
Title:
A Simulation-Based Digital Twin for Model-Driven Health Monitoring and Predictive Maintenance of an Automotive Braking System
DOI:
http://dx.doi.org/10.3384/ecp1713235
References:

ANSYS, Inc, Canonsburg, PA. (2016). Maxwell2D. http://www.ansys.com.


ANSYS, Inc, Canonsburg, PA. (2016). Maxwell3D. http://www.ansys.com.


ANSYS, Inc, Canonsburg, PA. (2016). Mechanical. http://www.ansys.com


ANSYS, Inc, Canonsburg, PA. (2016). SCADE Suite. http://www.ansys.com.


ANSYS, Inc, Canonsburg, PA. (2016). Simplorer. http://www.ansys.com.


GE, Boston, MA. (2016). How a ‘Digital Twin’ for physical assets can help achieve no unplanned downtime [Online]. Available: http://www.geglobalresearch.com/impact/how-a-digital-twin-for-physical-assets-can-helpachieve-no-unplanned-downtime


J.F. Archard. Wear theory and mechanisms. Wear control handbook. Peterson MB, Winer WO, editors. New York ASME, 1980.


Donald C. Augustin, Mark S. Fineberg, Bruce B. Johnson, Robert N. Linebarger, F. John Sansom, and Jon C. Strauss. The SCi Continuous System Simulation Language (CSSL). Simulation, No 9, pp. 281‚Äď303, 1967.


M. V. K. Chari, Z. J. Csendes. Finite Element Analysis of the Skin Effect in Current Carrying Conductors. IEEE Transactions on Magnetics, 13(5): 1125-1127, September 1977.


Iain S. Duff and John K. Reid. An Implementation of Tarjan’s Algorithm for the Block Triangularization of a Matrix. ACM Transactions on Mathematical Software, 4(2):137‚Äď147, 1978.


W.N Fu, P. Zhou, D. Lin, S. Stanton, Z.J. Cendes. Magnetic force computation in permanent magnets using a local energy coordinate derivative method. IEEE Trans. On Magnetics, 40(2): 683-686, 2004. doi: https://doi.org/10.1109/TMAG.2004.824774


S. Holland. Integrated Vehicle Health Management in the Automotive Industry. Health Management, Krzysztof Smigorski (Ed.). InTech, 2010. doi: https://doi.org/10.5772/9889.


J.E. Lenz. A review of magnetic sensors. Proc. of the IEEE, 78(6): 973-989, 1990. doi: https://doi.org/10.1109/5.56910


T. R. McGuire. Anisotropic magnetoresistance in ferromagnetic 3d alloys. IEEE Trans. Magn., 11(4), 1018‚Äď1038, 1975.


Modelon AB, Lund, Sweden. (2016). OPTIMICA Compiler Toolkit. http://www.modelon.com/products/optimicacompiler-toolkit/


Modelon AB, Lund, Sweden. (2016). Hydraulics Library. http://www.modelon.com/products/modelicalibraries/hydraulics-library/


Modelon AB, Lund, Sweden. (2016). Pneumatics Library. http://www.modelon.com/products/modelicalibraries/pneumatics-library/


Modelon AB, Lund, Sweden. (2016). Vehicle Dynamics Library. http://www.modelon.com/products/modelicalibraries/vehicle-dynamics-library/


Eric Obrochta. (2015, Dec. 5). Saturn S series ‚Äď unwanted ABS activation at all speeds. [YouTube video]. Available: https://www.youtube.com/watch?v=oGwyrLxtaNY&t=368s. Accessed Dec. 15, 2016.


Pacejka, H.B., and Bakker, E. (1993): The Magic Formula tyre model. Proceedings of 1st Colloquium on Tyre Models for Vehicle Analysis, Delft 1991, ed. H.B. Pacejka, Suppl. Vehicle System Dynamics, 21, 1993.


PTC, Needham, MA. (2016). Thingworx Analytics. http://www.ptc.com/internet-of-things/analytics.


R. Prytz. Machine Learning Methods for Vehicle Predictive Maintenance using Off-Board and On-Board Data. Halmstad University Dissertations, No. 9, 2014.Siemens, Munich, GmBH (2016). The Digital Twin [Online]. Available: https://www.siemens.com/customermagazine/en/home/industry/digitalization-in-machinebuilding/the-digital-twin.html


H. H. Woodson and J. R. Melcher. Electromechanical Dynamics: Part I: Discrete Systems. New York, NY: John Wiley & Sons, 1968.

Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, May 15-17, 2017

Author:
Ryan Magargle, Lee Johnson, Padmesh Mandloi, Peyman Davoudabadi, Omkar Kesarkar, Sivasubramani Krishnaswamy, John Batteh, Anand Pitchaikani
Title:
A Simulation-Based Digital Twin for Model-Driven Health Monitoring and Predictive Maintenance of an Automotive Braking System
DOI:
http://dx.doi.org/10.3384/ecp1713235
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