Operational modal analysis based prediction of actual stress in an offshore structural model

Tarpø, Marius; Nabuco, Bruna; Skafte, Anders; Kristoffersen, Julie; Vestermark, Jonas; Amador, Sandro; Brincker, Rune

doi:10.1016/j.proeng.2017.09.234

Cited by 14 publications

(3 citation statements)

References 8 publications

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“…The estimated stresses were found to be in good agreement with those calculated from the strain gauges. A similar study to estimate stresses in an off-shore structure under operational conditions using modal expansion and vibration data was performed by Tarpø et al [4,5].…”

Section: Introductionmentioning

confidence: 95%

An optimal sensor placement strategy for reliable expansion of mode shapes under measurement noise and modelling error

Jaya

Ceravolo

Fragonara

et al. 2020

Journal of Sound and Vibration

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Modal expansion techniques are typically used to expand the experimental modal displacements at sensor positions to all unmeasured degrees of freedom. Since in most cases, sensors can be attached only at limited locations in a structure, an expansion is essential to determine mode shapes, strains, stresses, etc. throughout the structure which can be used for structural health monitoring. Conventional sensor placement algorithms are mostly aimed to make the modal displacements at sensor positions of different modes as linearly independent as possible. However, under the presence of modelling errors and measurement noise, an optimal location based on this criterion is not guaranteed to provide an expanded mode shape which is close to the real mode shape. In this work, the expected value of normal distance between the real mode shape and the expanded mode shape is used as a measure of closeness between the two entities. Optimal sensor locations can be determined by minimizing this distance. This new criterion is applied on a simple cantilever beam and an industrial milling tower. In both cases, by using an exhaustive search of all possible sensor configurations it was possible to find sensor locations which resulted in a significant reduction in the distance when compared to a conventional optimal sensor placement strategy. Sufficiently accurate sub-optimal sequential sensor placement algorithm is also suggested as an alternative to the exhaustive search which is then compared with a genetic algorithmbased search. The efficiency of this new sensor placement criterion is further verified using Monte Carlo simulations for some realistic modelling error conditions.

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Section: Introductionmentioning

confidence: 95%

An optimal sensor placement strategy for reliable expansion of mode shapes under measurement noise and modelling error

Jaya

Ceravolo

Fragonara

et al. 2020

Journal of Sound and Vibration

View full text Add to dashboard Cite

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“…Given the non-stationary structural behavior of wind turbines, it is very challenging to establish a vibration-based damage detection and strain approach based on Operational Modal Analysis (OMA [6]) whereby the variation of the modal properties and the estimated strains are assessed over time. Such approach can be easily implemented not only to detect damage in stationary structural systems as described in many scientific publications as, for instance, [2][3][4], but also for full-field train estimation by making use of virtual sensing [5,7].…”

Section: Introductionmentioning

confidence: 99%

Vibration-based FE-model updating for strain history estimation of a 3MW offshore wind turbine tower

Amador,

Rasmussen,

Brincker

et al. 2024

J. Phys.: Conf. Ser.

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At present, the fatigue-life design of Offshore Wind Turbines (OWTs) is based on design assumptions accounting for significant uncertainties both on loading and fatigue-resistance of different vulnerable structural details. Monitoring of strain histories can potentially have a significant positive impact when estimating the remaining life of OWTs, as the uncertainties are reduced. However, strain-based monitoring approaches present the important drawbacks, that only instrumented locations can be assessed and that there tend to be accessibility issues associated with the installation process. In this paper, the initial results of a novel methodology developed to estimate strain time histories are presented. The underlying idea of such an approach is to combine an updated FE model with the vibration response histories acquired with only a few sensors to estimate the strain histories at any location of an OWT tower.

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“…To address the problems accompanying the TMA and EMA, the operational modal analysis, also known as outputonly analysis or ambient excitation, is established and widely used in applications involving towers, buildings, bridges, and off-shore platforms. [10][11][12][13] OMA utilizes the response, from structures operating in their ambient natural environment, to estimate the modal parameters. OMA is both fast to conduct and cost-effective.…”

Section: Introductionmentioning

confidence: 99%

Continuous video stream pixel sensor: A CNN‐LSTM based deep learning approach for mode shape prediction

Yang

Singh

Tavakkoli

et al. 2021

Structural Contr & Hlth

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Modal analysis has emerged as a globally accepted tool to formulate and optimize the behavioral functions of engineering structures, which assists in assessing structural failure and laying out a plan for their maintenance. Modal analysis aims at determining the frequencies, damping ratios, and mode shapes of the system under excitation. However, conventional mode shape measurement methods like contact sensors are prone to precision and accuracy issues owing to the sensor's weight and low spatial resolution. In this paper, we improve upon various existing methods for mode shape determination and introduce the idea of a full‐field pixel sensor for mode shape prediction. The proposed computer vision‐based deep learning architecture predicts the mode shape of a vibrating structure with significant precision. Besides, a ModeShape dataset consisting of the vibration recording video and finite element analysis (FEA) based label has been curated. Specifically, we introduce a convolutional neural network, long short‐term memory (CNN‐LSTM) computer vision‐based non‐contact vibration measurement technique for automated mode shape prediction. The key idea is to use each pixel of a RGB camera as a sensor and process the captured spatio‐temporal data to enable mode shape prediction. Our CNN‐LSTM model takes the video streams of a vibrating structure as input and yields the fundamental mode shapes. The proposed technique is non‐invasive and can extract information at relatively high spatial density. The CNN‐LSTM model is proficient by utilizing experimental outcomes. The robustness of the deep learning model has been scrutinized by utilizing specimens of an assortment of different materials and fluctuating dimensions.

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Operational modal analysis based prediction of actual stress in an offshore structural model

Cited by 14 publications

References 8 publications

An optimal sensor placement strategy for reliable expansion of mode shapes under measurement noise and modelling error

An optimal sensor placement strategy for reliable expansion of mode shapes under measurement noise and modelling error

Vibration-based FE-model updating for strain history estimation of a 3MW offshore wind turbine tower

Continuous video stream pixel sensor: A CNN‐LSTM based deep learning approach for mode shape prediction

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