Online Identification of a Mechanical System in the Frequency Domain with Short-Time DFT
A proper system identification method is of great importance in the process of acquiring an analytical model that adequately represents the characteristics of the monitored system. While the use of different time-domain online identification techniques has been widely recognized as a powerful approa...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Norwegian Society of Automatic Control
2015-07-01
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| Series: | Modeling, Identification and Control |
| Subjects: | |
| Online Access: | http://www.mic-journal.no/PDF/2015/MIC-2015-3-3.pdf |
| _version_ | 1818330578337923072 |
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| author | Niko Nevaranta Jukka Parkkinen Tuomo Lindh Markku Niemelä Olli Pyrhönen Juha Pyrhönen |
| author_facet | Niko Nevaranta Jukka Parkkinen Tuomo Lindh Markku Niemelä Olli Pyrhönen Juha Pyrhönen |
| author_sort | Niko Nevaranta |
| collection | DOAJ |
| description | A proper system identification method is of great importance in the process of acquiring an analytical model that adequately 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 for 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 flexible two-mass mechanical system with varying dynamics, and a particular attention is paid to detect the changes in the system dynamics. An online identification method is presented that is based on a recursive Kalman filter configured to perform like a discrete Fourier transform (DFT) at a selected set of frequencies. The experimental online identification results are compared with the corresponding values obtained from the offline-identified frequency responses. The results show an acceptable agreement and demonstrate the feasibility of the proposed identification method. |
| first_indexed | 2024-12-13T13:06:11Z |
| format | Article |
| id | doaj.art-3813e799b56d4d62971775befc75004e |
| institution | Directory Open Access Journal |
| issn | 0332-7353 1890-1328 |
| language | English |
| last_indexed | 2024-12-13T13:06:11Z |
| publishDate | 2015-07-01 |
| publisher | Norwegian Society of Automatic Control |
| record_format | Article |
| series | Modeling, Identification and Control |
| spelling | doaj.art-3813e799b56d4d62971775befc75004e2022-12-21T23:44:49ZengNorwegian Society of Automatic ControlModeling, Identification and Control0332-73531890-13282015-07-0136315716510.4173/mic.2015.3.3Online Identification of a Mechanical System in the Frequency Domain with Short-Time DFTNiko NevarantaJukka ParkkinenTuomo LindhMarkku NiemeläOlli PyrhönenJuha PyrhönenA proper system identification method is of great importance in the process of acquiring an analytical model that adequately 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 for 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 flexible two-mass mechanical system with varying dynamics, and a particular attention is paid to detect the changes in the system dynamics. An online identification method is presented that is based on a recursive Kalman filter configured to perform like a discrete Fourier transform (DFT) at a selected set of frequencies. The experimental online identification results are compared with the corresponding values obtained from the offline-identified frequency responses. The results show an acceptable agreement and demonstrate the feasibility of the proposed identification method.http://www.mic-journal.no/PDF/2015/MIC-2015-3-3.pdfKalman filterNonparametric estimationOnline identificationShort-time DFTTwo-mass system |
| spellingShingle | Niko Nevaranta Jukka Parkkinen Tuomo Lindh Markku Niemelä Olli Pyrhönen Juha Pyrhönen Online Identification of a Mechanical System in the Frequency Domain with Short-Time DFT Modeling, Identification and Control Kalman filter Nonparametric estimation Online identification Short-time DFT Two-mass system |
| title | Online Identification of a Mechanical System in the Frequency Domain with Short-Time DFT |
| title_full | Online Identification of a Mechanical System in the Frequency Domain with Short-Time DFT |
| title_fullStr | Online Identification of a Mechanical System in the Frequency Domain with Short-Time DFT |
| title_full_unstemmed | Online Identification of a Mechanical System in the Frequency Domain with Short-Time DFT |
| title_short | Online Identification of a Mechanical System in the Frequency Domain with Short-Time DFT |
| title_sort | online identification of a mechanical system in the frequency domain with short time dft |
| topic | Kalman filter Nonparametric estimation Online identification Short-time DFT Two-mass system |
| url | http://www.mic-journal.no/PDF/2015/MIC-2015-3-3.pdf |
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