Structural Health Monitoring of Wind Turbine Blades

Li, Hui; Zhou, Wensong; Xu, Jinlong

doi:10.1007/978-3-319-08413-8_9

Cited by 9 publications

(7 citation statements)

References 47 publications

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“…However, they exhibit anisotropic properties that make the mechanism behind failures sophisticated. This type of material usually suffers by aging and fatigue Moreover, even a small impact can lead to the creation of cracks, delamination phenomena on the fibers, etc., in situ sensors with intelligent algorithms for online damage detection can be combined in order to achieve high accuracy and reliability for damage identification and monitoring at the minimum cost [49].…”

Section: Assessment Of Shm Implementationmentioning

confidence: 99%

“…Indeed, AI is able to discover difficult correlations between signal and damage patterns, like ones appearing in wave propagation and ultrasound produced by distributed actuators and sensors and nondestructive evaluation (NDT), see [34][35][36][37][38][39][40][41][42]. From the many applications reported in the literature, one can mention here, for example, the aircraft icing detection and characterization problem [43], the prediction of urban gas consumption [44], the underwater backscatter recognition [45], the sonar classifier [46], the classification of marine mammals [47][48], the financial accounting information processing [49], and the biomedical application on breast cancer diagnosis [50].…”

Section: Introductionmentioning

confidence: 99%

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Identification of Damage in a Wind Turbine Blade Using Mechanical Measurements and Artificial Neural Networks

Koutsianitis,

Paterakis,

E. Stavroulakis

2024

JDSIS

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Due to the stochastic nature of environmental loadings, a lot of interest is paid in the discovery of possible damages of the involved equipment in modern industry. In wind turbines’ blades, the development of a smart structural health monitoring system is essential. In this paper, a large-scale composite wind turbine blade model is designed and used for the detection of several damage scenarios. The process is mainly based on the development of monitoring techniques which exploit the capabilities of artificial neural networks. These techniques can provide the exact position of possible damages, under given external loading scenarios. Moreover, the use of such methods decreases significantly the need of external intervention and at the same time it increases the accuracy of the whole approach. The above processes are simulated using the finite element method. The goal is to develop a neural network which realizes the correlation of measurements with damage patterns. The goal is focused on the solution of inverse problems involving elastically deformable structures, based on remote mechanical measurements. The correlation between measurements and damages, which is much more complicated in comparison to image analysis, is studied by means of neural networks.

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Section: Assessment Of Shm Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of Damage in a Wind Turbine Blade Using Mechanical Measurements and Artificial Neural Networks

Koutsianitis,

Paterakis,

E. Stavroulakis

2024

JDSIS

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“…In recent years, deep learning network architectures have been developed considerably, such as AE [18], deep belief networks (DBNs) [19,20], recurrent neural networks [21][22][23][24], and generative adversarial neural networks [25,26], have been used to solve different diagnostic tasks. Pan et al [27] proposed an improved CNN and LSTM-based approach for bearing fault diagnosis by combining the one-dimensional CNN and LSTM into a unified structure for identifying bearing fault types.…”

Section: Introductionmentioning

confidence: 99%

Transfer learning for bearing fault diagnosis: adaptive batch normalization and combined optimization method

Li,

Su,

et al. 2024

Meas. Sci. Technol.

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Bearings are crucial components in rotating machinery equipment. Bearing fault diagnosis plays a significant role in the maintenance of mechanical equipment. In practical industrial settings, equipment conditions often vary continuously, making it challenging to collect data for all operating conditions for bearing fault diagnosis. This paper proposes a transfer learning approach for bearing fault diagnosis based on Adaptive Batch Normalization (AdaBN) and a combined optimization algorithm. Initially, a ResNet neural network is trained using source domain data. Subsequently, the trained model is transferred to the target domain, where AdaBN is applied to mitigate domain shift issues. Furthermore, a combined optimization algorithm is employed during model training to enhance fault diagnosis accuracy. Experimental validation is conducted using bearing data from the CWRU dataset and NEFU dataset. Comparison shows that AdaBN and the combined optimization algorithm improve bearing fault diagnosis accuracy effectively. On the NEFU dataset, the diagnostic accuracy exceeds 95%.

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“…The vibro-acoustic structural health monitoring (SHM) technique is one of the most pragmatic methods used to identify damage in blades. Various vibration measurements can be applied to WT blades, such as active vibrations [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] and passive vibrations [1,[20][21][22][23][24]. In active vibrations, the vibrations are artificially generated using an electrodynamic shaker or actuator.…”

Section: Introductionmentioning

confidence: 99%

Wideband Vibro-Acoustic Modulation for Crack Detection in Wind Turbine Blades

Alnutayfat,

Sutin

2023

Applied Sciences

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Wind turbines (WT) are a popular method used in energy production, but blade failure and maintenance costs pose significant challenges for the industry. Early detection of blade defects is vital to prevent collapse. This paper examines the modulation of blade vibrations via low-frequency blade rotation, mirroring the vibro-acoustic modulation (VAM) method. Specifically, we study the modulation of blade vibrations, which are generated via blade interactions with air turbulence and have a wide frequency range. These vibrations are modulated by the alternating bending stress experienced during blade rotation. For the simulation of VAM, we employ a simple breathing crack model, which considers a mechanical oscillator with parameters that are periodically changed in response to low-frequency blade rotation. The modulation of the wideband signal by blade rotation can be extracted using the detection of envelope modulation on noise (DEMON) algorithm. This model was applied for the estimation of the modulation of a large (52-m-long) WT blade. Steel specimens have been used in laboratory experiments to demonstrate the feasibility of VAM using a probe broadband noise signal. This paper presents the first work to experimentally and theoretically apply wideband signals in VAM. It further explores the analysis of the use of natural vibrations within VAM for the SHM of WT blades.

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Structural Health Monitoring of Wind Turbine Blades

Cited by 9 publications

References 47 publications

Identification of Damage in a Wind Turbine Blade Using Mechanical Measurements and Artificial Neural Networks

Identification of Damage in a Wind Turbine Blade Using Mechanical Measurements and Artificial Neural Networks

Transfer learning for bearing fault diagnosis: adaptive batch normalization and combined optimization method

Wideband Vibro-Acoustic Modulation for Crack Detection in Wind Turbine Blades

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