Review of Structural Health Monitoring of Wind Turbine Blades Based on Vibration and Acoustic Measurement

doi:10.3901/JME.2024.07.079

Abstract

Abstract: Health conditions of blades are significant to the efficiency and reliability of wind power generation, and blades occupy an important position in both costs and damage probability among all components in wind turbines. Recently, plenty of studies have been reported in the field of structural health monitoring of wind turbine blades. In addition, structural health monitoring methods based on vibration and acoustic measurement have advantages in sensing ranges and data processing. Therefore, available structural health monitoring methods for wind turbine blades are firstly provided. Then, vibration-based investigations are reviewed in terms of inherent property, health index, statistical modelling, and machine learning, and acoustic-based investigations are reviewed in terms of independent microphone and microphone array. Finally, review conclusions are given, and future research directions in structural health monitoring of wind turbine blades are prospected.

Key words: structural health monitoring, wind turbine, blade, vibration, acoustics

CLC Number:

TM315

SUN Shilin, WANG Tianyang, CHU Fulei. Review of Structural Health Monitoring of Wind Turbine Blades Based on Vibration and Acoustic Measurement[J]. Journal of Mechanical Engineering, 2024, 60(7): 79-92.

References

[1] 唐新姿,顾能伟,黄轩晴,等. 风电功率短期预测技术研究进展[J]. 机械工程学报,2022,58(12):213-236. TANG Xinzi,GU Nengwei,HUANG Xuanqing,et al. Progress on short term wind power forecasting technology[J]. Journal of Mechanical Engineering,2022,58(12):213-236.
[2] 齐玮,董文静,高歌. 双碳目标下全球风电设备贸易网络格局演变分析[J]. 工业技术经济,2022,41(8):109-115. QI Wei,DONG Wenjing,GAO Ge. Analysis on the evolution of global wind power equipment trade network pattern under the double carbon target[J]. Journal of Industrial Technological Economics,2022,41(8):109-115.
[3] 陈雪峰,郭艳婕,许才彬,等. 风电装备故障诊断与健康监测研究综述[J]. 中国机械工程,2020,31(2):175-189. CHEN Xuefeng,GUO Yanjie,XU Caibin,et al. Review of fault diagnosis and health monitoring for wind power equipment[J]. China Mechanical Engineering,2020,31(2):175-189.
[4] 周勃,马天畅,俞方艾,等. 风力机叶片蒙皮微裂纹早期损伤演化分析[J]. 机械设计与制造,2021(5):40-43. ZHOU Bo,MA Tianchang,YU Fangai,et al. Early damage evolution analysis of micro-cracks on the skin of wind turbine blade[J]. Machinery Design & Manufacture,2021(5):40-43.
[5] 何赟泽,李响,王洪金,等. 基于可见光和热成像的风机叶片全周期无损检测综述[J]. 机械工程学报,2023,59(6):32-45. HE Yunze,LI Xiang,WANG Hongjin,et al. A review:full-cycle nondestructive testing based on visible light and thermography of wind turbine blade[J]. Journal of Mechanical Engineering,2023,59(6):32-45.
[6] 李成良,杨超,倪爱清,等. 复合材料在大型风电叶片上的应用与发展[J]. 复合材料学报,2023,40(3):1274-1284. LI Chengliang,YANG Chao,NI Aiqing,et al. Application and development of composite materials in large-scale wind turbine blade[J]. Acta Materiae Compositae Sinica,2023,40(3):1274-1284.
[7] DU Y,ZHOU S,JING X,et al. Damage detection techniques for wind turbine blades:A review[J]. Mechanical Systems and Signal Processing,2020,141:106445.
[8] 陈炜镒,胡伟飞,方健豪,等. 考虑雨滴侵蚀的风力发电机叶片涂层疲劳寿命优化设计方法[J]. 机械工程学报,2022,58(17):2-15. CHEN Weiyi,HU Weifei,FANG Jianhao,et al. Design optimization for coating fatigue life of wind turbine blades considering rain erosion[J]. Journal of Mechanical Engineering,2022,58(17):2-15.
[9] 许淳瑶,葛立超,冯红翠,等. 风力发电现状及叶片组成与回收利用综述[J]. 热力发电,2022,51(09):29-41. XU Chunyao,GE Lichao,FENG Hongcui,et al. Review on status of wind power generation and composition and recycling of wind turbine blades[J]. Thermal Power Generation,2022,51(9):29-41.
[10] 李垚,朱才朝,陶友传,等. 风电机组可靠性研究现状与发展趋势[J]. 中国机械工程,2017,28(09):1125-1133. LI Yao,ZHU Caichao,TAO Youchuan,et al. Research status and development tendency of wind turbine reliability[J]. China Mechanical Engineering,2017,28(09):1125-1133.
[11] LI D,HO S M,SONG G,et al. A review of damage detection methods for wind turbine blades[J]. Smart Materials and Structures,2015,24(3):33001.
[12] YANG R,HE Y,ZHANG H. Progress and trends in nondestructive testing and evaluation for wind turbine composite blade[J]. Renewable and Sustainable Energy Reviews,2016,60:1225-1250.
[13] KAEWNIAM P,CAO M,ALKAYEM N F,et al. Recent advances in damage detection of wind turbine blades:A state-of-the-art review[J]. Renewable and Sustainable Energy Reviews,2022,167:112723.
[14] TCHERNIAK D,MØLGAARD L L. Active vibration-based structural health monitoring system for wind turbine blade:Demonstration on an operating Vestas V27 wind turbine[J]. Structural Health Monitoring,2017,16(5):536-550.
[15] SUN S,WANG T,YANG H,et al. Adversarial representation learning for intelligent condition monitoring of complex machinery[J]. IEEE Transactions on Industrial Electronics,2023,70(5):5255-5265..
[16] HOELL S,OMENZETTER P. Improved damage detectability in a wind turbine blade by optimal selection of vibration signal correlation coefficients[J]. Structural Health Monitoring,2016,15(6):685-705.
[17] CHEN B,YU S,YU Y,et al. Acoustical damage detection of wind turbine blade using the improved incremental support vector data description[J]. Renewable Energy,2020,156:548-557.
[18] BEALE C,INALPOLAT M,NIEZRECKI C. Active acoustic damage detection of structural cavities using internal acoustic excitations[J]. Structural Health Monitoring,2019,19(1):48-65.
[19] SUN S,WANG T,YANG H,et al. Damage identification of wind turbine blades using an adaptive method for compressive beamforming based on the generalized minimax-concave penalty function[J]. Renewable Energy,2022,181:59-70.
[20] 黎少辉,蔡利梅. 基于气动信号分析的风机叶片裂纹故障识别[J]. 振动与冲击,2017,36(19):227-231. LI Shaohui,CAI Limei. Fan blade crack fault diagnosis based on the analysis of pneumatic signals[J]. Journal of Vibration and Shock,2017,36(19):227-231.
[21] YANG R,HE Y,ZHANG H. Progress and trends in nondestructive testing and evaluation for wind turbine composite blade[J]. Renewable and Sustainable Energy Reviews,2016,60:1225-1250.
[22] YE J,CHU C,CAI H,et al. A multi-scale model for studying failure mechanisms of composite wind turbine blades[J]. Composite Structures,2019,212:220-229.
[23] SUN S,WANG T,YANG H,et al. Condition monitoring of wind turbine blades based on self-supervised health representation learning:A conducive technique to effective and reliable utilization of wind energy[J]. Applied Energy,2022,313:118882.
[24] 李刚,胡红利,李亚妮. 风力发电机叶片状态监测与故障诊断技术近况[J]. 工业仪表与自动化装置,2017(5):16-20. LI Gang,HU Hongli,LI Yani. Review on recent development of technology of monitoring & diagnosis of wind turbine generator blades[J]. Industrial Instrumentation & Automation,2017(5):16-20.
[25] 杨家欢,宗哲英,王祯,等. 风机叶片检测的研究现状及进展[J]. 复合材料科学与工程,2020(6):109-113. YANG Jiahuan,ZONG Zheying,WANG Zhen,et al. A review of research on detecting the wind turbine blades[J]. Composites Science and Engineering,2020(6):109-113.
[26] SUN S,WANG T,CHU F. In-situ condition monitoring of wind turbine blades:A critical and systematic review of techniques,challenges,and futures[J]. Renewable and Sustainable Energy Reviews,2022,160:112326.
[27] WANG Y,LIANG M,XIANG J. Damage detection method for wind turbine blades based on dynamics analysis and mode shape difference curvature information[J]. Mechanical Systems and Signal Processing,2014,48(1):351-367.
[28] ZHANG C,MOUSAVI A A,MASRI S F,et al. Vibration feature extraction using signal processing techniques for structural health monitoring:A review[J]. Mechanical Systems and Signal Processing,2022,177:109175.
[29] 谢双义,金鑫. 风力发电机噪声传播的数值仿真[J]. 噪声与振动控制,2014,34(4):189-191. XIE Shuangyi,JIN Xin. Numerical simulation of noise emission of wind turbines[J]. Noise and Vibration Control,2014,34(4):189-191.
[30] KRAUSE T,OSTERMANN J. Damage detection for wind turbine rotor blades using airborne sound[J]. Structural Control and Health Monitoring,2020,27(5):e2520.
[31] SUN S,WANG T,YANG H,et al. Damage identification of wind turbine blades using the microphone array under different parametric and measuring conditions:A prototype study with laboratory-scale models[J]. Structural Health Monitoring,2023,22(1):201-215.
[32] SIERRA-PÉREZ J,TORRES-ARREDONDO M A,GÜEMES A. Damage and nonlinearities detection in wind turbine blades based on strain field pattern recognition. FBGs,OBR and strain gauges comparison[J]. Composite Structures,2016,135:156-166.
[33] 王炳楷,孙文磊,王宏伟,等. 风力机叶片表面应变的光纤光栅检测方法研究[J]. 机械科学与技术,2021,40(11):1741-1746. WANG Bingkai,SUN Wenlei,WANG Hongwei,et al. Study on detection method of blade surface strain wind turbine with Fiber Bragg Grating[J]. Mechanical Science and Technology for Aerospace Engineering,2021,40(11):1741-1746.
[34] WANG L,ZHANG Z. Automatic detection of wind turbine blade surface cracks based on UAV-Taken images[J]. IEEE Transactions on Industrial Electronics,2017,64(9):7293-7303.
[35] GUO J,LIU C,CAO J,et al. Damage identification of wind turbine blades with deep convolutional neural networks[J]. Renewable Energy,2021,174:122-133.
[36] 龚妙,李录平,刘瑞,等. 基于运行参数特征的风力机叶片覆冰诊断方法[J]. 动力工程学报,2019,39(3):214-219. GONG Miao,LI Luping,LIU Rui,et al. Diagnosis of ice accretion on wind turbine blades based on the features of operating parameters[J]. Journal of Chinese Society of Power Engineering,2019,39(03):214-219.
[37] 余建国,欧阳丁杰. 基于SCADA的风力机叶片PHM系统设计与实现[J]. 机械设计与研究,2021,37(6):229-233. YU Jianguo,OUYANG Dingjie. Design and implementation of PHM system for wind turbine blade based on SCADA[J]. Machine Design and Research,2021,37(6):229-233.
[38] YANG L,WANG L,ZHENG Z,et al. A continual learning-based framework for developing a single wind turbine cybertwin adaptively serving multiple modeling tasks[J]. IEEE Transactions on Industrial Informatics,2021,18(7):4912-4921.
[39] 沈学利,杨莹,秦鑫宇,等. 基于残差神经网络的风机叶片结冰故障诊断[J]. 噪声与振动控制,2022,42(1):79-87. SHEN Xueli,YANG Ying,QIN Xinyu,et al. Icing fault diagnosis of wind turbine blades based on residual neural network[J]. Noise and Vibration Control,2022,42(1):79-87.
[40] FATTAHI S J,ZABIHOLLAH A,ZAREIE S. Vibration monitoring of wind turbine blade using Fiber Bragg Grating[J]. Wind Engineering,2010,34(6):721-731.
[41] OCHIENG F X,HANCOCK C M,ROBERTS G W,et al. A review of ground-based radar as a noncontact sensor for structural health monitoring of in-field wind turbines blades[J]. Wind Energy,2018,21(12):1435-1449.
[42] KHADKA A,FICK B,AFSHAR A,et al. Non-contact vibration monitoring of rotating wind turbines using a semi-autonomous UAV[J]. Mechanical Systems and Signal Processing,2020,138:106446.
[43] 顾永强,冯锦飞,贾宝华,等. 损伤风机叶片模态频率变化规律的试验研究[J]. 噪声与振动控制,2020,40(3):84-87. GU Yongqiang,FENG Jinfei,JIA Baohua,et al. Experimental study on the variation law of modal frequencies of damaged blades of wind turbines[J]. Noise and Vibration Control,2020,40(3):84-87.
[44] 周经纬. 水平轴风力发电机叶片叶轮系统的动力学与控制[D]. 北京:北京工业大学,2020. ZHOU Jingwei. Dynamics and vibration control of a horizontal axis wind turbine blade and rotor[D]. Beijing:Beijing University of Technology,2020.
[45] 夏遵平,王彤. 谐波噪声下的试验模态分析[J]. 工程力学,2018,35(3):235-241. XIA Zunping,WANG Tong. Experimental modal analysis in consideration of harmonic noise[J]. Engineering Mechanics,2018,35(3):235-241.
[46] 顾永强,王晨. 小型风力发电机叶片损伤识别试验研究[J]. 内蒙古科技大学学报,2022,41(2):108-112. GY Yongqiang,WANG Chen. Experimental study on damage identification of small wind turbine blade[J]. Journal of Inner Mongolia University of Science and Technology,2022,41(2):108-112.
[47] 李录平,李芒芒,晋风华,等. 振动检测技术在风力机叶片裂纹故障监测中的应用[J]. 热能动力工程,2013,28(2):207-212. LI Luping,LI Mangmang,JIN Fenghua,et al. Applications of the vibration detection technologies in monitoring the blade crack fault of wind turbines[J]. Journal of Engineering for Thermal Energy & Power,2013,28(2):207-212.
[48] LI X Z,YUE X B,DONG X J,et al. Response transmissibility versus power spectrum density transmissibility:Dynamic property analysis and comparison[J]. Journal of Sound and Vibration,2019,454:32-50.
[49] KIM H,KIM M,CHOE D. Structural health monitoring of towers and blades for floating offshore wind turbines using operational modal analysis and modal properties with numerical-sensor signals[J]. Ocean Engineering,2019,188:106226.
[50] PACHECO-CHÉRREZ J,PROBST O. Vibration-based damage detection in a wind turbine blade through operational modal analysis under wind excitation[J]. Materials Today:Proceedings,2022,56:291-297.
[51] ULRIKSEN M D,TCHERNIAK D,KIRKEGAARD P H,et al. Operational modal analysis and wavelet transformation for damage identification in wind turbine blades[J]. Structural Health Monitoring,2015,15(4):381-388.
[52] OU Y,TATSIS K E,DERTIMANIS V K,et al. Vibration-based monitoring of a small-scale wind turbine blade under varying climate conditions. Part I:An experimental benchmark[J]. Structural Control and Health Monitoring,2021,28(6):e2660.
[53] LORENZO E D,PETRONE G,MANZATO S,et al. Damage detection in wind turbine blades by using operational modal analysis[J]. Structural Health Monitoring,2016,15(3):289-301.
[54] ULRIKSEN M D,TCHERNIAK D,HANSEN L M,et al. In-situ damage localization for a wind turbine blade through outlier analysis of stochastic dynamic damage location vector-induced stress resultants[J]. Structural Health Monitoring,2016,16(6):745-761.
[55] TCHERNIAK D. Rotor anisotropy as a blade damage indicator for wind turbine structural health monitoring systems[J]. Mechanical Systems and Signal Processing,2016,74:183-198.
[56] ZHOU H,HUANG X,WEN G,et al. Construction of health indicators for condition monitoring of rotating machinery:A review of the research[J]. Expert Systems with Applications,2022,203:117297.
[57] ABOUHNIK A,ALBARBAR A. Wind turbine blades condition assessment based on vibration measurements and the level of an empirically decomposed feature[J]. Energy Conversion and Management,2012,64:606-613.
[58] YANG W,LANG Z,TIAN W. Condition monitoring and damage location of wind turbine blades by frequency response transmissibility analysis[J]. IEEE Transactions on Industrial Electronics,2015,62(10):6558-6564.
[59] GONZÁLEZ A G,FASSOIS S D. A supervised vibration-based statistical methodology for damage detection under varying environmental conditions & its laboratory assessment with a scale wind turbine blade[J]. Journal of Sound and Vibration,2016,366:484-500.
[60] REZAEI M M,BEHZAD M,MORADI H,et al. Modal-based damage identification for the nonlinear model of modern wind turbine blade[J]. Renewable Energy,2016,94:391-409.
[61] AL-HADAD M,MCKEE K K,HOWARD I. Vibration characteristic responses due to transient mass loading on wind turbine blades[J]. Engineering Failure Analysis,2019,102:187-202.
[62] 李雅峰,徐玉秀. 基于模态应变能变化率的大型风力发电机叶片损伤识别与定位[J]. 太阳能学报,2015,36(9):2251-2256. LI Yafeng,XU Yuxiu. Damage identification and positioning of wind turbine blade based on element modal strain energy change ratio[J]. Acta Energiae Solaris Sinica,2015,36(9):2251-2256.
[63] 靳子洋. 风机叶片裂纹损伤状态识别技术研究[D]. 上海:上海电机学院,2016. JIN Ziyang. The study of the identification technology in wind turbine blade crack damage status[D]. Shanghai:Shanghai Dianji University,2016.
[64] 吴琪强,郭帅平,王钢,等. 基于固有频率的风力机叶片裂纹精确定位与程度识别[J]. 振动与冲击,2019,38(24):18-27. WU Qiqiang,GUO Shuaiping,WANG Gang,et al. Accurate location and degree identification of wind turbine blade cracks based on natural frequency[J]. Journal of Vibration and Shock,2019,38(24):18-27.
[65] 刘楠. 基于变分模态分解的风力发电机组叶轮不平衡检测方法[J]. 电子测量技术,2021,44(24):147-152. LIU Nan. The method of detecting unbalance of the wind turbine rotor based on variation mode decomposition[J]. Electronic Measurement Technology,2021,44(24):147-152.
[66] 顾永强,冯锦飞,张哲玮,等. 基于模态参数的在役风力发电机叶片损伤识别研究[J]. 太阳能学报,2022,43(3):350-355. GU Yongqiang,FENG Jinfei,ZHANG Zhewei,et al. Research on blade damage identification of active wind turbine based on modal parameters[J]. Acta Energiae Solaris Sinica,2022,43(3):350-355.
[67] WANG X,LIU Z,ZHANG L,et al. Wavelet package energy transmissibility function and its application to wind turbine blade fault detection[J]. IEEE Transactions on Industrial Electronics,2022,69(12):13597-13606.
[68] KHOSHMANESH S,WATSON S J,ZAROUCHAS D. New indicator for damage localization in a thick adhesive joint of a composite material used in a wind turbine blade[J]. Engineering Structures,2023,283:115870.
[69] FREMMELEV M A,LADPLI P,ORLOWITZ E,et al. A full-scale wind turbine blade monitoring campaign:detection of damage initiation and progression using medium-frequency active vibrations[J]. Structural Health Monitoring,2023,22(6):4171-4193..
[70] XU J,DING X,GONG Y,et al. Rotor imbalance detection and quantification in wind turbines via vibration analysis[J]. Wind Engineering,2021,46(1):3-11.
[71] JARAMILLO F,GUTIÉRREZ J M,ORCHARD M,et al. A Bayesian approach for fatigue damage diagnosis and prognosis of wind turbine blades[J]. Mechanical Systems and Signal Processing,2022,174:109067.
[72] HOU R,XIA Y. Review on the new development of vibration-based damage identification for civil engineering structures:2010-2019[J]. Journal of Sound and Vibration,2021,491:115741.
[73] TAYLOR S G,FARINHOLT K M,PARK G,et al. Application of a wireless sensor node to health monitoring of operational wind turbine blades[M]. New York:Springer,2011.
[74] HOELL S,OMENZETTER P. Optimal selection of autoregressive model coefficients for early damage detectability with an application to wind turbine blades[J]. Mechanical Systems and Signal Processing,2016,70-71:557-577.
[75] AVENDANO-VALENCIA L D,CHATZI E N,TCHERNIAK D. Gaussian process models for mitigation of operational variability in the structural health monitoring of wind turbines[J]. Mechanical Systems and Signal Processing,2020,142:106686.
[76] GAO F,WU X,LIU Q,et al. Fault simulation and online diagnosis of blade damage of large-scale wind turbines[J]. Energies,2019,12(3).
[77] WANG X,ZHANG L,HEATH W P. Wind turbine blades fault detection using system identification-based transmissibility analysis[J]. Insight-Non-Destructive Testing and Condition Monitoring,2022,64(3):164-169.
[78] XU M,LI J,WANG S,et al. Damage detection of wind turbine blades by Bayesian multivariate cointegration[J]. Ocean Engineering,2022,258:111603.
[79] PANAGIOTOPOULOS A,DMITRI T,SPILIOS F D. Damage detection on the blade of an operating wind turbine via a single vibration sensor and statistical time series methods:Exploring the performance limits of robust methods[J]. Structural Health Monitoring,2022,22(1):433-448.
[80] 雷亚国,贾峰,周昕,等. 基于深度学习理论的机械装备大数据健康监测方法[J]. 机械工程学报,2015,51(21):49-56. LEI Yaguo,JIA Feng,ZHOU Xin,et al. A deep learning-based method for machinery health monitoring with big data[J]. Journal of Mechanical Engineering,2015,51(21):49-56.
[81] 雷亚国,贾峰,孔德同,等. 大数据下机械智能故障诊断的机遇与挑战[J]. 机械工程学报,2018,54(05):94-104. LEI Yaguo,JIA Feng,KONG Detong,et al. Opportunities and challenges of machinery intelligent fault diagnosis in big data era[J]. Journal of Mechanical Engineering,2018,54(05):94-104.
[82] DERVILIS N,CHOI M,TAYLOR S G,et al. On damage diagnosis for a wind turbine blade using pattern recognition[J]. Journal of Sound and Vibration,2014,333(6):1833-1850.
[83] MOVSESSIAN A,GARCÍA CAVA D,TCHERNIAK D. An artificial neural network methodology for damage detection:Demonstration on an operating wind turbine blade[J]. Mechanical Systems and Signal Processing,2021,159:107766.
[84] 谭滔. 基于LabVIEW的海上风力机叶片远程状态监测系统设计与开发[D]. 长沙:长沙理工大学,2017. TAN Tao. Design and development of the remote condition monitoring system for offshore wind turbine blade based on LabVIEW[D]. Changsha:Changsha University of Science & Technology,2017.
[85] JOSHUVA. A,SUGUMARAN. V. A data driven approach for condition monitoring of wind turbine blade using vibration signals through best-first tree algorithm and functional trees algorithm:A comparative study[J]. ISA Transactions,2017,67:160-172.
[86] JOSHUVA A,SUGUMARAN V. A lazy learning approach for condition monitoring of wind turbine blade using vibration signals and histogram features[J]. Measurement,2020,152:107295.
[87] KHAZAEE M,DERIAN P,MOURAUD A. A comprehensive study on structural health monitoring (SHM) of wind turbine blades by instrumenting tower using machine learning methods[J]. Renewable Energy,2022,199:1568-1579.
[88] 赵娟,陈斌,李永战,等. 复杂背景噪声下风机叶片裂纹故障声学特征提取方法[J]. 北京邮电大学学报,2017,40(05):117-122. ZHAO Juan,CHEN Bin,LI Yongzhan,et al. Acoustical crack feature extraction of turbine blades under complex background noise[J]. Journal of Beijing University of Posts and Telecommunications,2017,40(05):117-122.
[89] ZHAO Z,CHEN N. Acoustic emission based damage source localization for structural digital twin of wind turbine blades[J]. Ocean Engineering,2022,265:112552.
[90] PAN X,LIU Z,XU R,et al. Early warning of damaged wind turbine blades using spatial-temporal spectral analysis of acoustic emission signals[J]. Journal of Sound and Vibration,2022,537:117209.
[91] LIU P F,CHEN H Y,WU T,et al. Fatigue life evaluation of offshore composite wind turbine blades at Zhoushan Islands of China using wind site data[J]. Applied Composite Materials,2023,30(4):1097-1122.
[92] ZHANG Y,CUI Y,XUE Y,et al. Modeling and measurement study for wind turbine blade trailing edge cracking acoustical detection[J]. IEEE Access,2020,8:105094-105103.
[93] 蔡巧巧. 风力发电机叶片缺陷声信号处理及识别技术研究[D]. 武汉:华中科技大学,2021. CAI Qiaoqiao. Research on sound signal processing and recognition technology of defect of wind turbine blade[D]. Wuhan:Huazhong University of Science & Technology,2021.
[94] CHEN B,ZHANG M,LIN Z,et al. Acoustic-based whistle detection of drain hole for wind turbine blade[J]. ISA Transactions,2022,131:736-747.
[95] SOLIMINE J,INALPOLAT M. An unsupervised data-driven approach for wind turbine blade damage detection under passive acoustics-based excitation[J]. Wind Engineering,2022,46(4):1311-1330.
[96] TSAI T,WANG C. Acoustic-based method for identifying surface damage to wind turbine blades by using a convolutional neural network[J]. Measurement Science and Technology,2022,33(8):85601.
[97] REGAN T,BEALE C,INALPOLAT M. Wind turbine blade damage detection using supervised machine learning algorithms[J]. Journal of Vibration and Acoustics,2017,139(6).
[98] SUN S,WANG T,CHU F,et al. Acoustic source identification using an off-grid and sparsity-based method for sound field reconstruction[J]. Mechanical Systems and Signal Processing,2022,170:108869.
[99] CHEN W,PENG B,LIEM R P,et al. Experimental study of airfoil-rotor interaction noise by wavelet beamforming[J]. The Journal of the Acoustical Society of America,2020,147(5):3248-3259.
[100] 杨洋,褚志刚. 高性能波束形成声源识别方法研究综述[J]. 机械工程学报,2021,57(24):166-183. YANG Yang,CHU Zhigang. A review of high-performance beamforming methods for acoustic source identification[J]. Journal of Mechanical Engineering,2021,57(24):166-183.
[101] 初宁,黄乾,余亮,等. 一种基于相位平均的旋转声源高分辨率定位方法[J]. 振动与冲击,2021,40(19):125-136. CHU Ning,HUANG Qian,YU Liang,et al. A high-resolution positioning method of rotating sound source based on phase average[J]. Journal of Vibration and Shock,2021,40(19):125-136.
[102] POOZESH P,AIZAWA K,NIEZRECKI C,et al. Structural health monitoring of wind turbine blades using acoustic microphone array[J]. Structural Health Monitoring,2016,16(4):471-485.