Tilt errors of translational accelerometers attached to dynamic systems with tilt motion caused by the system response

Tarpø, Marius; Nabuco, Bruna; Boroschek, Rubén; Brincker, Rune

doi:10.1016/j.jsv.2021.115967

Cited by 5 publications

(9 citation statements)

References 8 publications

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“…It should be noted that a tilt error occurs in the measurement data due to gravitational acceleration (e.g. Tarpø et al, 2021). This is not taken into account in the evaluation.…”

Section: Calibration Of Tower Vibrations Of a Wind Turbinementioning

confidence: 99%

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

et al. 2022

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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.

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“…It should be noted that a tilt error occurs in the measurement data due to gravitational acceleration (e.g. Tarpø et al, 2021). This is not taken into account in the evaluation.…”

Section: Calibration Of Tower Vibrations Of a Wind Turbinementioning

confidence: 99%

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

et al. 2022

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“…In this study, we apply a high‐pass filter to all data with a cut‐off frequency of 0.2 Hz to exclude the quasi‐static response of the wind turbine. The geophones are calibrated using digital correlation, 30 and we use the vertical geophones to reduce the tilt effects on the geophones 31 . We use the integration theorem for the Fourier transformation 32 to integrate signals from the geophones into displacement.…”

Section: Case Studymentioning

confidence: 99%

“…The geophones are calibrated using digital correlation, 30 and we use the vertical geophones to reduce the tilt effects on the geophones. 31 We use the integration theorem for the Fourier transformation 32 to integrate signals from the geophones into displacement. Afterwards, we divide the data into three frequency bands: 0.2-0.7, 0.7-4.5 and 4.5-10 Hz using complementary filters.…”

Section: 2 | Wind Turbinementioning

confidence: 99%

Data‐driven virtual sensing and dynamic strain estimation for fatigue analysis of offshore wind turbine using principal component analysis

et al. 2021

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Virtual sensing enables estimation of stress in unmeasured locations of a system using a system model, physical sensors and a process model. The system model holds the relationship between the physical sensors and the desired stress response. A process model processes both the physical sensors and the system model to synthesise virtual sensors that ‘measure’ the desired stress response. Thus, virtual sensing enables mapping between the physical sensors (input) and the desired stress response (output). The system model is a mathematical model of the system based on knowledge or data of the system. Here, the data‐driven system model is constructed directly on data analyses for the specific system. In this paper, supervised learning and data‐driven system models are applied to strain estimation of an offshore wind turbine in the dynamic range through a novel use of principal component analysis (PCA); 40 min of training data is used to establish the data‐driven system model that can estimate the dynamic strain response with high precision for 2 months, while the estimated fatigue damage averaged out to −1.76% of the measured strain response.

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“…To exclude the quasi-static response of the wind turbine, we apply a high-pass filtered to all data with a cut-off frequency of 0.2 Hz. We calibrate the geophones using digital correlation [21] and we reduce the tilt effects on the geophones through the vertical geophones [22]. We integrate signals from the geophones into displacement by the integration theorem for the Fourier transformation [23].…”

Section: Wind Turbinementioning

confidence: 99%

Virtual Sensing and Strain Estimation on an Offshore Wind Turbine Using Supervised Learning

Tarpø¹,

Amador²,

Katsanos³

et al. 2021

Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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Virtual sensing enables expansion and transformation of measured quantities from physical sensors into new quantities at unmeasured locations. This allows for estimating the strain and stress in unmeasured locations of a system by transforming the physical sensors (input) into the desired strain and stress response (output). This transformation model can be based on either knowledge of the systems, data from the system, or any combination of these. In this paper, supervised learning and data-driven models are applied to strain estimation of an offshore wind turbine through Principal Component Analysis (PCA). Training data are used to establish the data-driven model that enables a versatile strain estimation that functions well under different wind scenarios than the training data set.

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Tilt errors of translational accelerometers attached to dynamic systems with tilt motion caused by the system response

Cited by 5 publications

References 8 publications

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

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

Data‐driven virtual sensing and dynamic strain estimation for fatigue analysis of offshore wind turbine using principal component analysis

Virtual Sensing and Strain Estimation on an Offshore Wind Turbine Using Supervised Learning

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