Very low frequency IEPE accelerometer calibration and application to a wind energy structure

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Jonscher, C.; Hofmeister, B.; Grießmann, T.; Rolfes, R.: Very low frequency IEPE accelerometer calibration and application to a wind energy structure. In: Wind energy science : WES 7 (2022), Nr. 3, S. 1053-1067. DOI: https://doi.org/10.5194/wes-7-1053-2022

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/12750

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Sum total of downloads: 129




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Abstract: 
In this work, we present an experimental setup for very low frequency calibration measurements of low-noise integrated electronics piezoelectric (IEPE) accelerometers and a customised signal conditioner design for using IEPE sensors down to 0.05 Hz. AC-response IEPE accelerometers and signal conditioners have amplitude and phase deviations at low frequencies. As the standard calibration procedure in the low-frequency range is technically challenging, IEPE accelerometers with standard signal conditioners are usually used in frequency ranges above 1 Hz. Vibrations on structures with low eigenfrequencies like wind turbines are thus often monitored using DC-coupled micro-electro-mechanical system (MEMS) capacitive accelerometers. This sensor type suffers from higher noise levels compared to IEPE sensors. To apply IEPE sensors instead of MEMS sensors, in this work the calibration of the entire measurement chain of three different IEPE sensors with the customised signal conditioner is performed with a low-frequency centrifuge. The IEPE sensors are modelled using infinite impulse response (IIR) filters to apply the calibration to time-domain measurement data of a wind turbine support structure. This procedure enables an amplitude and phase-accurate vibration analysis with IEPE sensors in the low-frequency range down to 0.05 Hz.
License of this version: CC BY 4.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2022
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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pos. country downloads
total perc.
1 image of flag of Germany Germany 86 66.67%
2 image of flag of United States United States 18 13.95%
3 image of flag of China China 7 5.43%
4 image of flag of Italy Italy 3 2.33%
5 image of flag of Tanzania, United Republic of Tanzania, United Republic of 2 1.55%
6 image of flag of Finland Finland 2 1.55%
7 image of flag of Singapore Singapore 1 0.78%
8 image of flag of Serbia Serbia 1 0.78%
9 image of flag of Netherlands Netherlands 1 0.78%
10 image of flag of Korea, Republic of Korea, Republic of 1 0.78%
    other countries 7 5.43%

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