Modeling and Measurement Study for Wind Turbine Blade Trailing Edge Cracking Acoustical Detection

Zhang, Yang; Cui, Yuanzhen; Xue, Yu; Liu, Yan

doi:10.1109/access.2020.2999783

Cited by 9 publications

(5 citation statements)

References 31 publications

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“…Using distances x 1 = 225 mm, x 2 = 237 mm (frequency range of 22-38 kHz, ∆x/λ = 0.3) and x 1 = 225 mm and x 2 = 231 mm (frequency range of 57-64 kHz, ∆x/λ = 0.3) of the A 0 mode, the two segments of the dispersion curves were reconstructed. The comparison of the results with 2D-FFT algorithm based on peaks of spectrum magnitude was performed, the mean relative error δ c ph = 1.3% for first reconstructed range (22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38) and δ c ph = 0.3% for second range (57-64 kHz) was determined. The total mean relative error δ c ph = 0.8% was calculated.…”

Section: Discussionmentioning

confidence: 99%

“…Wind energy is said to be one of the most basic energy sustainability alternatives, therefore, the world is moving rapidly towards renewable energy, and the number of wind farms is constantly growing [28][29][30]. However, with the increasing number of wind farms, the world is facing an ever-increasing number of turbine blades being ejected [31]. The main reasons for this are various defects that occur in the production of WTBs, and the service life can affect the development of damage under fatigue loads [32,33].…”

Section: Introductionmentioning

confidence: 99%

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Reconstruction of Lamb Wave Dispersion Curves in Different Objects Using Signals Measured at Two Different Distances

2021

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The possibilities of an effective method of two adjacent signals are investigated for the evaluation of Lamb waves phase velocity dispersion in objects of different types, namely polyvinyl chloride (PVC) film and wind turbine blade (WTB). A new algorithm based on peaks of spectrum magnitude is presented and used for the comparison of the results. To use the presented method, the wavelength-dependent parameter is proposed to determine the optimal distance range, which is necessary in selecting two signals for analysis. It is determined that, in the range of 0.17–0.5 wavelength where δcph is not higher than 5%, it is appropriate to use in the case of an A0 mode in PVC film sample. The smallest error of 1.2%, in the distance greater than 1.5 wavelengths, is obtained in the case of the S0 mode. Using the method of two signals analysis for PVC sample, the phase velocity dispersion curve of the A0 mode is reconstructed using selected distances x1 = 70 mm and x2 = 70.5 mm between two spatial positions of a receiving transducer with a mean relative error δcph=2.8%, and for S0 mode, x1 = 61 mm and x2 = 79.7 mm with δcph=0.99%. In the case of the WTB sample, the range of 0.1–0.39 wavelength, where δcph is not higher than 3%, is determined as the optimal distance range between two adjacent signals. The phase velocity dispersion curve of the A0 mode is reconstructed in two frequency ranges: first, using selected distances x1 = 225 mm and x2 = 231 mm with mean relative error δcph=0.3%; and second, x1 = 225 mm and x2 = 237 mm with δcph=1.3%.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reconstruction of Lamb Wave Dispersion Curves in Different Objects Using Signals Measured at Two Different Distances

2021

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“…So, we used the octave method to extract the feature information. Octave [24] is a macro signal analysis method that does not consider the amplitude of a specific frequency, but instead focuses on the power spectrum characteristics of the frequency band, which is composed of multiple frequencies. The steps for extracting the feature information using the octave method are as follows:…”

Section: Octave Feature Extractionmentioning

confidence: 99%

Wind Turbine Blade Defect Detection Based on Acoustic Features and Small Sample Size

et al. 2022

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Wind power has become an important source of electricity for both production and domestic use. However, because wind turbines often operate in harsh environments, they are prone to cracks, blisters, and corrosion of the blade surface. If these defects cannot be repaired in time, the cracks evolve into larger fractures, which can lead to blade rupture. As such, in this study, we developed a remote non-contact online health monitoring and warning system for wind turbine blades based on acoustic features and artificial neural networks. Collecting a large number of wind turbine blade defect signals was challenging. To address this issue, we designed an acoustic detection method based on a small sample size. We employed the octave to extract defect information, and we used an artificial neural network based on model-agnostic meta-learning (MAML-ANN) for classification. We analyzed the influence of locations and compared the performance of MAML-ANN with that of traditional ANN. The experimental results showed that the accuracy of our method reached 94.1% when each class contained only 50 data; traditional ANN achieved an accuracy of only 85%. With MAML-ANN, the training is fast and the global optimal solution is automatic searched, and it can be expanded to situations with a large sample size.

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“…Wind power blade trailing edge cracking (TEC) is generally an early blade health problem. Zhang et al [129] proposed a prediction method combining computational fluid dynamics (CFD) simulations and semiempirical models. An airfoil cross-sectional analysis of the blade trailing edge with and without a 3 mm gap crack defect successfully explained that blade trailing edge cracking is the cause of the accompanying acoustically sharp "whistling sound".…”

Section: Introduction Of Wind Power Blade Aerodynamic Noisementioning

confidence: 99%

Acoustic-Signal-Based Damage Detection of Wind Turbine Blades—A Review

Ding

Chao

Zhang

2023

Sensors

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Monitoring and maintaining the health of wind turbine blades has long been one of the challenges facing the global wind energy industry. Detecting damage to a wind turbine blade is important for planning blade repair, avoiding aggravated blade damage, and extending the sustainability of blade operation. This paper firstly introduces the existing wind turbine blade detection methods and reviews the research progress and trends of monitoring of wind turbine composite blades based on acoustic signals. Compared with other blade damage detection technologies, acoustic emission (AE) signal detection technology has the advantage of time lead. It presents the potential to detect leaf damage by detecting the presence of cracks and growth failures and can also be used to determine the location of leaf damage sources. The detection technology based on the blade aerodynamic noise signal has the potential of blade damage detection, as well as the advantages of convenient sensor installation and real-time and remote signal acquisition. Therefore, this paper focuses on the review and analysis of wind power blade structural integrity detection and damage source location technology based on acoustic signals, as well as the automatic detection and classification method of wind power blade failure mechanisms combined with machine learning algorithm. In addition to providing a reference for understanding wind power health detection methods based on AE signals and aerodynamic noise signals, this paper also points out the development trend and prospects of blade damage detection technology. It has important reference value for the practical application of non-destructive, remote, and real-time monitoring of wind power blades.

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Modeling and Measurement Study for Wind Turbine Blade Trailing Edge Cracking Acoustical Detection

Cited by 9 publications

References 31 publications

Reconstruction of Lamb Wave Dispersion Curves in Different Objects Using Signals Measured at Two Different Distances

Reconstruction of Lamb Wave Dispersion Curves in Different Objects Using Signals Measured at Two Different Distances

Wind Turbine Blade Defect Detection Based on Acoustic Features and Small Sample Size

Acoustic-Signal-Based Damage Detection of Wind Turbine Blades—A Review

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