[1] 2018年中国风电吊装容量统计简报[J]. 风能, 2019(4): 56-67. [2] 陈雪峰, 郭艳婕. 风电装备振动监测与诊断[M]. 北京: 科学出版社, 2016: 12-13. [3] 陈晨. 风电场风机叶片无人机巡检技术应用研究[J]. 风力发电, 2017(5): 49-52. [4] 陶鹏, 赵一中, 姚恩涛, 等. 基于敲击声振法的风机叶片脱层检测系统设计[J]. 测控技术, 2014, 33(4): 12-15. [5] 乌建中, 陶益. 基于短时傅里叶变换的风机叶片裂纹损伤检测[J]. 中国工程机械学报, 2014, 12(2): 180-183. [6] Gómez González A, 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. [7] David G, Dmitri T. An experimental study on the data-driven structural health monitoring of large wind turbine blades using a single accelerometer and actuator[J]. Mechanical Systems and Signal Processing, 2019, 127: 102-119. [8] 张保钦, 雷保珍, 赵林惠, 等. 风机叶片故障预测的振动方法研究[J]. 电子测量与仪器学报, 2014, 28(3): 285-291. [9] Aihara A, Kawaguchi T, Miki N, et al. A vibration estimation method for wind turbine blades[J]. Experimental Mechanics, 2017, 57(7): 1-12. [10] Aral S, Zhu M, Christopher N, et al. Vibration-based damage detection in wind turbine blades using phase-based motion estimation and motion magnification[J]. Journal of Sound and Vibration, 2018, 421: 300-318. [11] Schroeder K, Ecke W, Apitz J, et al.A fibre Bragg grating sensor system monitors operational load in a wind turbine rotor blade[J]. Measurement Science and Technology, 2006, 17(5): 1167. [12] Choi K S, Huh Y H, Kwon I B, et al. A tip deflection calculation method for a wind turbine blade using temperature compensated FBG sensors[J]. Smart Materials and Structures, 2012, 21(2): 025008. [13] 邢作霞, 薛田威, 张军阳. 一种叶根机械载荷测试方法的研究[J]. 可再生能源, 2013, 31(3): 73-76. [14] Huh Y H, Kim J, Hong S. Response of impedance measured by polyvinylidene fluoride film sensors to damage propagation for wind turbine blade[J]. Journal of Intelligent Material Systems and Structures, 2014, 25(5): 606-612. [15] Dutton A G, Blanch M, Vionis P, et al. Acoustic emission monitoring from wind turbine blades undergoing static fatigue testing[J]. INSIGHT, 2000, 42(12): 805-808. [16] 袁洪芳, 周璐, 柯细勇, 等. 基于声发射信号的风机叶片裂纹定位分析[J]. 计算机工程与设计, 2011, 32(1): 320-323. [17] 曲弋, 陈长征, 周昊, 等. 基于声发射和神经网络的风机叶片裂纹识别研究[J]. 机械设计与制造, 2012(3): 152-154. [18] 饶金根, 顾桂梅. 基于谐波小波包和支持向量机的风机叶片损伤识别研究[J]. 玻璃钢/复合材料, 2014(4): 37-41. [19] 周勃, 张士伟, 陈长征, 等. 风力机叶片多裂纹扩展声发射信号的特征识别[J]. 仪器仪表学报, 2015, 36(1): 110-117. [20] 岳大皓, 李晓丽, 张浩军, 等. 风电叶片红外热波无损检测的实验探究[J]. 红外技术, 2011, 33(10): 614-617. [21] Worzewski T, Krankenhagen R, Doroshtnasir M, et al. Thermographic inspection of a wind turbine rotor blade segment utilizing natural conditions as excitation source, Part Ⅰ: Solar excitation for detecting deep structures in GFRP[J]. Infrared Physics and Technology, 2016, 76. [22] 李怀富. 超声无损检测技术在风电叶片上的应用[C]//中国硅酸盐学会玻璃钢分会、《玻璃钢/复合材料》杂志社. 第十八届玻璃钢/复合材料学术年会论文集. 北京:《玻璃钢/复合材料》杂志社, 2010: 4. [23] 耿晓锋, 魏克湘, 王琼, 等. 基于多频简谐调制的风力机叶片裂纹检测研究[J]. 振动与冲击, 2018, 37(22): 201-205. [24] Tiwari K A, Raisutis R. Post-processing of ultrasonic signals for the analysis of defects in wind turbine blade using guided waves[J]. The Journal of Strain Analysis for Engineering Design, 2018(12). [25] 陆荣林, 费云鹏, 白宝泉. 微波检测原理及其在复合材料中的应用[J]. 玻璃钢/复合材料, 2001(2): 40-41. [26] Li Z, Haigh A, Soutis C, et al. Microwaves sensor for wind turbine blade inspection[J]. Applied Composite Materials, 2017, 24(2). [27] Arnold P, Moll J, Mlzer M, et al. Radar-based structural health monitoring of wind turbine blades: The case of damage localization[J]. Wind Energy, 2018, 21(8): 676-680. [28] Tippmann J D, Di Scalea F L. Passive-only damage detection by reciprocity of Green's functions reconstructed from diffuse acoustic fields with application to wind turbine blades[J]. Journal of Intelligent Material Systems and Structures, 2015, 26(10): 1251-1258. [29] 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(5): 117-122. [30] 董方旭, 王从科, 凡丽梅, 等. X射线CT成像检测方法对复合材料内部分层缺陷检测结果的影响研究[J]. 玻璃钢/复合材料, 2019(3): 86-91. [31] 徐阳, 刘卫生, 乔光辉. 兆瓦级大型风力发电机叶片的无损检测[J]. 玻璃钢/复合材料, 2013(3): 76-79. |