Mikel Gonzalez Cocho
IK4-Ikerlan Technology Research Center, Control and Monitoring Area, Spain / KU Leuven, Department of Mechanical Engineering, Belgium
Oscar Salgado
IK4-Ikerlan Technology Research Center, Control and Monitoring Area, Spain
Jan Croes
KU Leuven, Department of Mechanical Engineering, Belgium /Member of Flanders Make, Belgium
Bert Pluymers
KU Leuven, Department of Mechanical Engineering, Belgium /Member of Flanders Make, Belgium
Wim Desmet
KU Leuven, Department of Mechanical Engineering, Belgium /Member of Flanders Make, Belgium
Ladda ner artikelhttp://dx.doi.org/10.3384/ecp17132337Ingår i: Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, May 15-17, 2017
Linköping Electronic Conference Proceedings 132:37, s. 337-344
Publicerad: 2017-07-04
ISBN: 978-91-7685-575-1
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
This paper presents an application case for the estimation of forces using Modelica and the FMI. For that purpose model-based virtual sensors are used. These techniques are presented and the development of the virtual sensor for Modelica and the FMI is discussed. The work has been done in Python where the package pyFMI is used with models exported with the FMI 2.0 for model exchange. The technique is used for the estimation of forces and the friction coefficient in a vertical transportation system. The model of this test bench is explained and the results of the estimation of forces and the friction coefficient are discussed. These estimations provide a valuable tool for the condition monitoring of guiding systems.
K. Bizon, G. Continillo, S. Lombardi, E. Mancaruso, and B.M. Vaglieco. Ann-based virtual sensor for on-line prediction of in-cylinder pressure in a diesel engine. In 24th European Symposium on Computer Aided Process Engineering, volume 33 of Computer Aided Chemical Engineering, pages 763 – 768. Elsevier, 2014. doi: http://doi.org/10.1016/B978-0-444-63456-6.50128-9.
M. Bonvini, M. Wetter, and M. Sohn. An fmi-based framework for state and parameter estimation. In Proceedings of the 10 th International Modelica Conference; March 10-12; 2014; Lund; Sweden, number 096, pages 647–656. Linköping University Electronic Press, 2014.
J. Brembeck, M. Otter, and D. Zimmer. Nonlinear observers based on the functional mockup interface with applications to electric vehicles. In Proceedings of the 8th International Modelica Conference; March 20th-22nd; Technical Univeristy; Dresden; Germany, number 63, pages 474–483. Linköping University Electronic Press, 2011.
J. Brembeck, A. Pfeiffer, M. Fleps-Dezasse, M. Otter, K. Wernersson, and H. Elmqvist. Nonlinear state estimation with an extended fmi 2.0 co-simulation interface. In Proceedings of the 10th International Modelica Conference-Lund, Sweden-Mar 10-12, 2014, volume 96, pages 53–62. Linköping University Electronic Press, 2014.
E. Esteban, O. Salgado, A. Iturrospe, and I. Isasa. Model-based approach for elevator performance estimation. Mechanical Systems and Signal Processing, 68-69:125 – 137, 2016. ISSN 0888-3270. doi: http://doi.org/10.1016/j.ymssp.2015.07.005. URL http://www.sciencedirect.com/science/article/pii/S0888327015003246.
JCB Gonzaga, L.A.C. Meleiro, C Kiang, and R. Maciel Filho. Ann-based soft-sensor for real-time process monitoring and control of an industrial polymerization process. Computers & Chemical Engineering, 33(1):43–49, 2009.
I. Isasa. Model validation applied to locally nonlinear lift structures. PhD thesis, Mondragon Unibertsitatea, 2010.
R. Isermann. Model-based fault-detection and diagnosis status and applications. Annual Reviews in Control, 29(1):71 – 85, 2005. ISSN 1367-5788. doi: http://doi.org/10.1016/j.arcontrol.2004.12.002. URL https//www.sciencedirect.com/science/article/pii/S1367578805000052.
L. Janovsk`y. Elevator mechanical design. Elevator World Inc, 1999.
P. Kadlec, R. Grbi´c, and B. Gabrys. Review of adaptation mechanisms for data-driven soft sensors. Computers & chemical engineering, 35(1):1–24, 2011.
Xueqin Liu, Uwe Kruger, Tim Littler, Lei Xie, and Shuqing Wang. Moving window kernel pca for adaptive monitoring of nonlinear processes. Chemometrics and intelligent laboratory systems, 96(2):132–143, 2009.
F. Naets, J. Croes, and W. Desmet. An online coupled state/input/parameter estimation approach for structural dynamics. Computer Methods in Applied Mechanics and Engineering, 283:1167 – 1188, 2015. ISSN 0045-7825. doi: https://doi.org/10.1016/j.cma.2014.08.010. URL https://www.sciencedirect.com/science/article/pii/S0045782514002795.
P. Samara, J. Sakellariou, G. Fouskitakis, J. Hios, and S. Fassois. Aircraft virtual sensor design via a time-dependent functional pooling narx methodology. Aerospace Science and Technology, 29(1):114–124, 2013.
D. Simon. Optimal state estimation: Kalman, H infinity, and nonlinear approaches. John Wiley & Sons, 2006.
Z. Wan, S. Yi, K. Li, R. Tao, M. Gou, X. Li, and S. Guo. Diagnosis of elevator faults with ls-svm based on optimization by k-cv. Journal of Electrical and Computer Engineering, 2015: 70, 2015.
Y. Zhang, Z. Zhao, T. Lu, L. Yuan,W. Xu, and J. Zhu. A comparative study of luenberger observer, sliding mode observer and extended kalman filter for sensorless vector control of induction motor drives. In 2009 IEEE Energy Conversion Congress and Exposition, pages 2466–2473. IEEE, 2009.