STUDY ON MATERIAL DAMAGE CHARACTERISTICS OF WIND TURBINE BLADES BASED ON ACOUSTIC EMISSION b-VALUE ANALYSIS

Liao Lida; Lu Zhengpeng; Ye Han; Huang Bin; Shu Wangyong; Zhang Zhiming

doi:10.19912/j.0254-0096.tynxb.2022-1686

PDF(11073 KB)

Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (2) : 172-180. DOI: 10.19912/j.0254-0096.tynxb.2022-1686

STUDY ON MATERIAL DAMAGE CHARACTERISTICS OF WIND TURBINE BLADES BASED ON ACOUSTIC EMISSION b-VALUE ANALYSIS

Liao Lida¹, Lu Zhengpeng¹, Ye Han¹, Huang Bin^1,2, Shu Wangyong¹, Zhang Zhiming¹

Author information +

History +

Abstract

Damage to glass fiber epoxy resin reinforced composite （GFRP）, which is the main material of wind turbine blades, will lead to a reduction in blade life and even fracture, so damage detection must be carried out. The acoustic emission method can be used to analyze the damage characteristics of GFRP composite materials and realize online monitoring of blade performance. In this paper, the acoustic emission b-value feature is used to characterize the damage degree of GFRP, and the acoustic emission parameters are clustered by principal component analysis and the k-means++ algorithm to analyze the damage mode of GFRP. The results are as follows：1） The b-value analysis of the whole acoustic emission event shows that the b-value of the GFRP sample shows a significant downward trend before fracture, indicating that the change rate of the b-value can be used as an early warning signal of material fracture. 2） According to the proportion of nine acoustic emission parameters in the principal component analysis, the amplitude and peak frequency of the AE signal are selected. Through the k-means++ algorithm to cluster the characteristic parameters, it is found that the acoustic emission signals in the stretching process are divided into four clusters, and the characteristic frequency of each cluster is found. 3） The tensile fracture of the GFRP specimen is observed by scanning electron microscope （SEM）, and four damage modes corresponding to matrix cracking, fiber/matrix debonding, delamination and fiber fracture are obtained. The method based on acoustic emission b-value analysis is applicable to research on GFRP damage characteristics and can be popularized in the field of wind turbine blade damage detection.

Key words

wind turbine blades / acoustic emission / composite materials / b-value analysis

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Liao Lida, Lu Zhengpeng, Ye Han, Huang Bin, Shu Wangyong, Zhang Zhiming. STUDY ON MATERIAL DAMAGE CHARACTERISTICS OF WIND TURBINE BLADES BASED ON ACOUSTIC EMISSION b-VALUE ANALYSIS[J]. Acta Energiae Solaris Sinica. 2024, 45(2): 172-180 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1686

References

[1] DU Y, ZHOU S X, JING X J, et al.Damage detection techniques for wind turbine blades: a review[J]. Mechanical systems and signal processing, 2020, 141: 106445.
[2] NSENGIYUMVA W, ZHONG S C, LIN J W, et al.Advances, limitations and prospects of nondestructive testing and evaluation of thick composites and sandwich structures: a state-of-the-art review[J]. Composite structures, 2021, 256: 112951.
[3] AHMED O, WANG X, TRAN M V, et al.Advancements in fiber-reinforced polymer composite materials damage detection methods: towards achieving energy-efficient SHM systems[J]. Composites part B: engineering, 2021, 223: 109136.
[4] SAEEDIFAR M, ZAROUCHAS D.Damage characterization of laminated composites using acoustic emission: a review[J]. Composites part B: engineering, 2020, 195: 108039.
[5] GUTENBERG B.The energy of earthquakes[J]. Quarterly Journal of the Geological Society, 1956, 112(1/2/3/4): 1-14.
[6] SHIOTANI T, OHTSU M, IKEDA K.Detection and evaluation of AE waves due to rock deformation[J]. Construction and building materials, 2001, 15(5/6): 235-246.
[7] WU Y Q, LI S L, WANG D W, et al.Damage monitoring of masonry structure under in situ uniaxial compression test using acoustic emission parameters[J]. Construction and building materials, 2019, 215: 812-822.
[8] DONG L J, ZHANG L Y, LIU H N, et al.Acoustic emission b value characteristics of granite under true triaxial stress[J]. Mathematics, 2022, 10(3): 451.
[9] SAEEDIFAR M, NAJAFABADI M A, ZAROUCHAS D, et al.Barely visible impact damage assessment in laminated composites using acoustic emission[J]. Composites part B: engineering, 2018, 152: 180-192.
[10] JUNG D, YU W R, NA W.Use of acoustic emission b(Ib)-values to quantify damage in composites[J]. Composites communications, 2020, 22: 100499.
[11] ZHOU W, ZHAO W Z, ZHANG Y N, et al.Cluster analysis of acoustic emission signals and deformation measurement for delaminated glass fiber epoxy composites[J]. Composite structures, 2018, 195: 349-358.
[12] AZADI M, SAYAR H, GHASEMI-GHALEBAHMAN A, et al.Tensile loading rate effect on mechanical properties and failure mechanisms in open-hole carbon fiber reinforced polymer composites by acoustic emission approach[J]. Composites part B: engineering, 2019, 158: 448-458.
[13] MOHAMMADI R, NAJAFABADI M A, SAEEDIFAR M, et al.Correlation of acoustic emission with finite element predicted damages in open-hole tensile laminated composites[J]. Composites part B: engineering, 2017, 108: 427-435.
[14] BARILE C, CASAVOLA C, PAPPALETTERA G, et al.Laplacian score and K-means data clustering for damage characterization of adhesively bonded CFRP composites by means of acoustic emission technique[J]. Applied acoustics, 2022, 185: 108425.
[15] GUL S, TABRIZI I E, OKAN B S, et al.An experimental investigation on damage mechanisms of thick hybrid composite structures under flexural loading using multi-instrument measurements[J]. Aerospace science and technology, 2021, 117: 106921.
[16] PEI N, ZHOU S Y, XU C G, et al.Cluster analysis of acoustic emission signals for tensile damage characterization of quasi-static indented carbon/glass fiber-reinforced hybrid laminate composites[J]. Composites part A: applied science and manufacturing, 2021, 150: 106597.
[17] ZHAO W Z, ZHOU W.Cluster analysis of acoustic emission signals and tensile properties of carbon/glass fiber-reinforced hybrid composites[J]. Structural health monitoring, 2019, 18(5/6): 1686-1697.
[18] BARILE C, CASAVOLA C, PAPPALETTERA G, et al.Acoustic emission waveforms for damage monitoring in composite materials: shifting in spectral density, entropy and wavelet packet transform[J]. Structural health monitoring, 2022, 21(4): 1768-1789.
[19] HARIZI W, CHAKI S, BOURSE G, et al.Damage mechanisms assessment of glass fiber-reinforced polymer (GFRP) composites using multivariable analysis methods applied to acoustic emission data[J]. Composite structures, 2022, 289: 115470.
[20] BOHMANN T, SCHLAMP M, EHRLICH I.Acoustic emission of material damages in glass fibre-reinforced plastics[J]. Composites part B: engineering, 2018, 155: 444-451.
[21] ÖZASLAN E, YETGIN A, ACAR B, et al.Damage mode identification of open hole composite laminates based on acoustic emission and digital image correlation methods[J]. Composite structures, 2021, 274: 114299.
[22] SIKDAR S, LIU D Z, KUNDU A.Acoustic emission data based deep learning approach for classification and detection of damage-sources in a composite panel[J]. Composites part B: engineering, 2022, 228: 109450.
[23] LI W H, GUO S J, LIU Y D, et al.Structure health monitoring of composites joint reinforced by acoustic emission based smart composite fasteners[J]. Composites communications, 2022, 33: 101213.
[24] REFAHI OSKOUEI A, YOUSEFI J.Characterization of residual strength in transversely loaded glass-polyester composites by acoustic emission and sentry function[J]. Materials today: proceedings, 2018, 5(1): 381-387.
[25] CHELLIAH S K, PARAMESWARAN P, RAMASAMY S, et al.Optimization of acoustic emission parameters to discriminate failure modes in glass-epoxy composite laminates using pattern recognition[J]. Structural health monitoring, 2019, 18(4): 1253-1267.
[26] MAILLET E, BAKER C, MORSCHER G N, et al.Feasibility and limitations of damage identification in composite materials using acoustic emission[J]. Composites part A: applied science and manufacturing, 2015, 75: 77-83.
[27] HE X F, CAI D, NIYOGI P.Laplacian score for feature selection[C]//Proceedings of the 18th International Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, 2005: 507-514.
[28] JOLLIFFE I.Principal component analysis[A]. Encyclopedia of statistics in behavioral science, 2005, 3: 1580-1584.
[29] ARTHUR D, VASSILVITSKII S.K-means++: the advantages of careful seeding[R]. California: Stanford, 2007.
[30] ROUSSEEUW P J.Silhouettes: a graphical aid to the interpretation and validation of cluster analysis[J]. Journal of computational and applied mathematics, 1987, 20: 53-65.
[31] DAVIES D, BOULDIN D. A cluster separation measure[J]. IEEE transactions on pattern analysis and machine intelligence, 1979, PAMI-1(2): 224-227.