Advanced search
1 file | 1.22 MB

Online identification of a mechanical system in frequency domain using sliding DFT

Author
Organization
Abstract
A proper real-time system identification method is of great importance in order to acquire an analytical model that sufficiently represents the characteristics of the monitored system. While the use of different time-domain online identification techniques has been widely recognized as a powerful approach to system diagnostics, the frequency-domain identification techniques have primarily been considered for offline commissioning purposes. This paper addresses issues in the online frequency-domain identification of a mechanical system with varying dynamics; particular attention is paid to detect the changes in the system dynamics. A closed- loop online identification method is presented that is based on a Sliding Discrete Fourier Transform (SDFT) at a selected set of frequencies. The method is experimentally validated by a closed-loop controlled servomechanism with a limited stroke and time-varying parameters.
Keywords
DRIVE SYSTEM, Sliding DFT, Servomechanism, Nonparametric estimation, Online identification, FRICTION, DYNAMICS, OBSERVER, INERTIA

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 1.22 MB

Citation

Please use this url to cite or link to this publication:

Chicago
Nevaranta, Niko, Stijn Derammelaere, Jukka Parkkinen, Bram Vervisch, Tuomo Lindh, Kurt Stockman, Markku Niemelä, Olli Pyrhönen, and Juha Pyrhönen. 2016. “Online Identification of a Mechanical System in Frequency Domain Using Sliding DFT.” Ieee Transactions on Industrial Electronics 63 (9): 5712–5723.
APA
Nevaranta, N., Derammelaere, S., Parkkinen, J., Vervisch, B., Lindh, T., Stockman, K., Niemelä, M., et al. (2016). Online identification of a mechanical system in frequency domain using sliding DFT. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 63(9), 5712–5723.
Vancouver
1.
Nevaranta N, Derammelaere S, Parkkinen J, Vervisch B, Lindh T, Stockman K, et al. Online identification of a mechanical system in frequency domain using sliding DFT. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS. 2016;63(9):5712–23.
MLA
Nevaranta, Niko, Stijn Derammelaere, Jukka Parkkinen, et al. “Online Identification of a Mechanical System in Frequency Domain Using Sliding DFT.” IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 63.9 (2016): 5712–5723. Print.
@article{7224661,
  abstract     = {A proper real-time system identification method is of great importance in order to acquire an analytical model that sufficiently represents the characteristics of the monitored system. While the use of different time-domain online identification techniques has been widely recognized as a powerful approach to system diagnostics, the frequency-domain identification techniques have primarily been considered for offline commissioning purposes. This paper addresses issues in the online frequency-domain identification of a mechanical system with varying dynamics; particular attention is paid to detect the changes in the system dynamics. A closed- loop online identification method is presented that is based on a Sliding Discrete Fourier Transform (SDFT) at a selected set of frequencies. The method is experimentally validated by a closed-loop controlled servomechanism with a limited stroke and time-varying parameters.},
  author       = {Nevaranta, Niko and Derammelaere, Stijn and Parkkinen, Jukka and Vervisch, Bram and Lindh, Tuomo and Stockman, Kurt and Niemel{\"a}, Markku and Pyrh{\"o}nen, Olli and Pyrh{\"o}nen, Juha},
  issn         = {0278-0046},
  journal      = {IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS},
  keyword      = {DRIVE SYSTEM,Sliding DFT,Servomechanism,Nonparametric estimation,Online identification,FRICTION,DYNAMICS,OBSERVER,INERTIA},
  language     = {eng},
  number       = {9},
  pages        = {5712--5723},
  title        = {Online identification of a mechanical system in frequency domain using sliding DFT},
  url          = {http://dx.doi.org/10.1109/TIE.2016.2574303 Published: SEP 2016 View Journal Information},
  volume       = {63},
  year         = {2016},
}

Altmetric
View in Altmetric
Web of Science
Times cited: