Vibration-based structural health monitoring of a wind turbine system. Part I: Resonance phenomenon

Hu, Wei; Thöns, Sebastian; Rohrmann, R.; Said, Samir; Rücker, Werner

doi:10.1016/j.engstruct.2014.12.034

Cited by 93 publications

(46 citation statements)

References 23 publications

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“…Recent experimental work performed on a non-ideal eccentric motor mounted at the tip of a cantilever beam as reported in [35] supports the fact that theoretical predictions are very accurate in determining the exact power requirement to pass through critical speeds. Moreover, recent experimental result from a wind turbine system [36] has shown the evidence of Sommerfeld effect in rotor dynamic systems. However, for the considered rotor system, currently no experimental results are available with these authors.…”

Section: Discussionmentioning

confidence: 97%

Sommerfeld effect characterization in rotors with non-ideal drive from ideal drive response and power balance

Karthikeyan

Bisoi

Samantaray

et al. 2015

Mechanism and Machine Theory

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Section: Discussionmentioning

confidence: 97%

Sommerfeld effect characterization in rotors with non-ideal drive from ideal drive response and power balance

Karthikeyan

Bisoi

Samantaray

et al. 2015

Mechanism and Machine Theory

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“…After the Kalman gain is computed, it is possible to define the identified model in a forward innovation model: (6) where represents the white noise innovation sequence and is the acceleration recorded at time step. Transforming equations (6) into modal basis equations:…”

Section: Decomposition Of the Acceleration Time Series In Modal Accelmentioning

confidence: 99%

“…Some examples of implementation of monitoring systems based on OMA in wind turbines are presented in [5,6].…”

Section: Introductionmentioning

confidence: 99%

One Year of Continuous Dynamic Monitoring of a Wind Turbine. Structural Integrity and Fatigue Assessment

Cunha¹,

Oliveira²,

Magalhães³

et al. 2015

Proceedings of the 5th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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Abstract. The paper presents recent results from a research project based on a continuous dynamic monitoring of a wind turbine. The monitoring system was developed with the capacity to detect structural abnormalities based on the continuous tracking of the modal properties of wind turbines (natural frequencies, modal damping ratios and mode shapes INTRODUCTIONWind power exploitation has shown a consistent growth both in onshore [1] and offshore installations [2], with increasingly competitive costs. This growth has been reflected in the installation of wind turbines composed by very flexible support structures and with large rotors. Due to these characteristics, wind turbines became very susceptible to vibration problems and, consequently, to fatigue damage problems.This paper presents a continuous dynamic monitoring system, under development by the Laboratory of Vibrations and Structural Monitoring (ViBest, www.fe.up.pt/vibest) of FEUP, with the purpose of identifying structural changes (i.e. damage) and estimating the fatigue damage condition of a wind turbine. The system is based on the continuous tracking of modal properties of the structure (frequency values, modal damping ratios and mode shapes), identified during the different operating conditions of a wind turbine. The strategy adopted for the monitoring system has already proved to be suitable to be installed both in onshore and offshore wind turbines [3].The modal identification of large structures excited by operational conditions, an approach usually named as Operational Modal Analysis (OMA), is a technique widely used in several applications [4]. Some examples of implementation of monitoring systems based on OMA in wind turbines are presented in [5,6].The assessment of fatigue condition through acceleration data records was already investigated by some researchers [7,8]. However, in these works, the authors used the results from modal identification tests to tune a finite element model in order to estimate the stress condition of the structure. This paper presents some results already achieved with the developed dynamic monitoring. Initially, the results obtained with the continuous tracking of the modal properties of the wind turbine during one year are presented. In the second part, an improved methodology to estimate the stress time history of the tower structure is presented, where only the acceleration data, alongside with a simple stiffness matrix of the tower, is used. This methodology is applied to a numerical example using the HAWC2 aeroelastic code [9].

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“…SHM could be more useful for characterizing the evolution in time of this spatial variability. Most part of research efforts in SHM have focused on the spatial localization of defects or damage of structural components [Hu et al, 2015;Kuprapha & Warnitchai, 2012]. However, spatial variability characterization of loading or material properties from SHM data is still a challenge because of the finite number of sensors and the large number of potential positions of local failures or disruptions.…”

Section: Introductionmentioning

confidence: 99%

Optimal sensor placement for Spatial Variability Assessment of Structures

Schoefs¹,

Bastidas‐Arteaga²

2017

Proceedings of the Joint COST TU1402 - COST TU1406 - IABSE WC1 Workshop: The Value of Structural Health Monitoring for the Reli

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Abstract. Structural reliability assessment is largely influenced by the spatial variability of material properties or defaults; however, there are still various challenges for their characterization and modeling. Structural Health Monitoring (SHM) could provide useful information in space and time for spatial variability characterization of material properties and mechanical solicitations; nevertheless, this challenge is arduous because of the large number of potential sensor positions of local disruptions/failures. This paper proposes a methodology to optimize the spatial distribution of embedded sensors used for spatial variability assessment of stationary random fields. The optimization criterion relies on the width of the confidence interval of statistics for the characteristics to identify. For sake of simplicity, the paper illustrates the method for one-dimensional problems. The proposed method is applied firstly to a numerical example were several hypothetical structural configurations that could be found in practice are studied. It is finally applied to two case studies (a reinforced concrete beam and a steel wharf) where water content and loss of steel thickness are respectively measured. The results show that the stationary property is useful to deduce the minimum quantity of sensors and their position for a given quality requirement. They also allow us to propose a criterion for defining if regular or non-regular spacing of sensors along the inspection zone is more appropriate depending on the component length and autocorrelation structure of the random field.

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Vibration-based structural health monitoring of a wind turbine system. Part I: Resonance phenomenon

Cited by 93 publications

References 23 publications

Sommerfeld effect characterization in rotors with non-ideal drive from ideal drive response and power balance

Sommerfeld effect characterization in rotors with non-ideal drive from ideal drive response and power balance

One Year of Continuous Dynamic Monitoring of a Wind Turbine. Structural Integrity and Fatigue Assessment

Optimal sensor placement for Spatial Variability Assessment of Structures

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