University of Oulu

Neisi, N., Nieminen, V., Kurvinen, E., Lämsä, V., & Sopanen, J. (2022). Estimation of Unmeasurable Vibration of a Rotating Machine Using Kalman Filter. Machines, 10(12), 1116.

Estimation of unmeasurable vibration of a rotating machine using Kalman filter

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Author: Neisi, Neda1; Nieminen, Vesa2; Kurvinen, Emil3;
Organizations: 1Department of Mechanical Engineering, Lappeenranta-Lahti University of Technology, Yliopistonkatu 34, FI-53850 Lappeenranta, Finland
2VTT Technical Research Centre of Finland Ltd., Kivimiehentie 3, P.O. Box 1000, FI-02044 Espoo, Finland
3Materials and Mechanical Engineering, Department of Mechanical Engineering, University of Oulu, P.O. Box 4200, FI-90014 Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 9.7 MB)
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Language: English
Published: Multidisciplinary Digital Publishing Institute, 2022
Publish Date: 2023-06-08


Rotating machines are typically equipped with vibration sensors at the bearing location and the information from these sensors is used for condition monitoring. Installing additional sensors may not be possible due to limitations of the installation and cost. Thus, the internal condition of machines might be difficult to evaluate. This study presents a numerical and experimental study on the case of a rotor supported by four rolling element bearings (REBs). As such, the study resembles a complex real-life industrial multi-fault scenario: a lack of information, uncertainties, and nonlinearities increase the overall complexity of the system. The study provides a methodology for modeling and analyzing complicated systems without prior information. First, the unknown model parameters of the system are approximated using measurement data and the linearized model. Thereafter, the Unscented Kalman Filter (UKF) is applied to the estimation of the vibration characteristics in unmeasured locations. As a result, the estimation of unmeasured vibration characteristics has a reasonable agreement with the rotor whirling, and the estimated results are within a 95% confidence interval. The proposed methodology can be considered as a transfer learning method that can be further used in other identification problems in the field of rotating machinery.

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Series: Machines
ISSN: 2075-1702
ISSN-E: 2075-1702
ISSN-L: 2075-1702
Volume: 10
Issue: 12
Article number: 1116
DOI: 10.3390/machines10121116
Type of Publication: A1 Journal article – refereed
Field of Science: 214 Mechanical engineering
Funding: Digibuzz-LUT and Digibuzz-VTT projects (decision no. 4329/31/2019 and 4437/31/2019).
Copyright information: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (