Abstract
This paper presents a model-based monitoring approach for the estimation of the elevator rail friction forces. This model-based monitoring approach is based on a Linear Parameter Varying (LPV) model of a 1:1 elevator installation, comprising both the mechanical and the electrical subsystems. The Extended Kalman algorithm (EKF) is then employed as an observer for the joint estimation of the elevator LPV states and parameters. Finally, the estimated rail friction forces are evaluated and energy efficiency indicators describing elevator performance during the ride are obtained.
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Notes
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ReBEL is a Matlab toolkit for Sequential Bayesian inference.
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Acknowledgments
This work has been supported in part by the project ETORTEK MECOFF, economically supported by The Basque Government under project No. IE13-379. Any opinions, findings and conclusions expressed in this article are those of the authors and do not necessarily reflect the views of funding agencies. The authors also gratefully acknowledge Orona EIC S. Coop. for supporting this research line.
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Esteban, E., Salgado, O., Iturrospe, A., Isasa, I. (2016). Model-Based Estimation of Elevator Rail Friction Forces. In: Chaari, F., Zimroz, R., Bartelmus, W., Haddar, M. (eds) Advances in Condition Monitoring of Machinery in Non-Stationary Operations. CMMNO 2014. Applied Condition Monitoring, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-20463-5_27
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DOI: https://doi.org/10.1007/978-3-319-20463-5_27
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