基于自适应VMD-WVD的风电叶片主梁层合板损伤演化识别

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Abstract

Damage mode is one of the most intuitive ways to characterize the damage evolution state.The time-frequency analysis technique can be used to describe the change law of the damage mode in the time-frequency domain, so as to determine the damage evolution state at different stages.However, the existence of cross terms seriously interferes with the time-frequency characteristic information transmitted by various damage modes in the time-frequency analysis.In order to solve the above problems, a multi-component signal cross-term suppression time-frequency analysis method was proposed based on improved adaptive variational mode decomposition (AVMD) and Wigner-Ville distribution.In this method, the short-time Fourier transform was used to estimate the damage mode type and the center frequency spectrum in the acoustic emission signal.The optimal penalty factor was selected by evaluating the correlation between the VMD mode component frequency spectrum and the original signal center frequency spectrum, and then Wigner-Ville distribution of various damage modes was calculated.Experimental results show that the method is consistent with the results of electron microscope scanning and can effectively suppress the cross-term interference of Wigner-Ville distribution in various damage modes.

Key words： wind turbine blade defect evolution acoustic emission AVMD Wigner transform

Received: 21 February 2020 Published: 28 October 2021

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	Articles by authors
	ZHANG Yanan
	ZHOU Bo
	YU Fangai
	SHEN Chen

Cite this article:

ZHANG Yanan,ZHOU Bo,YU Fangai, et al. Damage evolution identification of wind turbine blade main beam laminate based on adaptive VMD-WVD[J]. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(20): 25-33.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2021/V40/I20/25

［1］杜文超.基于声发射技术的大型风力机叶片材料的损伤研究［D］.南京：南京航空航天大学, 2011.

［2］张旭，邢静忠.叶片局部损伤对大型水平轴风力机静动态特性影响的仿真分析［J］.工程力学，2013,30(2): 406-412.
ZHANG Xu, XING Jingzhong.Simulation analysis on the effect of local damage of blade on static and dynamic characteristics for large horizontal axis wind turbine［J］.Chinese Journal of Engineering Mechanics, 2013,30(2): 406-412.

［3］GUILHERME F G.The use of intelligent computational tools for damage detection and identification with an emphasis on composites-A review［J］.Composite Structures, 2018,196: 44-54.

［4］MOHAMMADI R, NAJAFABADI M A, SAEEDIFAR M.Correlation of acoustic emission with finite element predicted damages in open-hole tensile laminated composites［J］.Composites Part B, 2017,108: 427-435.

［5］BAKER C, MORSCHER G N, PUJAR V V, et al.Transverse cracking in carbon fiber reinforced polymer composites: modal acoustic emission and peak frequency analysis［J］.Composites Science and Technology, 2015,116: 26-32.

［6］WISNERA B, MAZURA K, PERUMALA V, et al.Acoustic emission signal processing framework to identify fracture in aluminum alloys［J］.Engineering Fracture Mechanics, 2019,210: 367-380.

［7］KHARRAT M, PLACET V, RAMASSO E, et al.Influence of damage accumulation under fatigue loading on the AE-based health assessment of composite materials: Wave distortion and AE-features evolution as a function of damage level［J］.Composites: Part A, 2018,109: 615-627.

［8］龚仁荣, 顾建祖, 骆英, 等.Gabor 小波时频分析在声发射信号处理中的应用［J］.中国测试技术, 2006,32(1): 76-79.
GONG Renrong, GU Jianzu, LUO Ying, et al.Application of gabor wavelet time-frequency analysis in signal processing of acoustic emission［J］.China Measurement Technology, 2006,32(1): 76-79.

［9］PENG Z K, CHU F L.Application of the wavelet transform in machine condition monitoring and fault giagnostics: a review with bibliography［J］.Mechanical Systems and Signal Processing, 2004,18(2): 199-221.

［10］张海滨，陆思良，何清波，等.多普勒畸变声学信号的伪时频分析及其校正［J］.振动与冲击, 2016,35(5): 15-21.
ZHANG Haibin, LU Siliang, HE Qingbo, et al.Fake time-frequency analysis of acoustic signals with Doppler distortion and its correction［J］.Chinese Journal of Vibration and Shock, 2016,35(5): 15-21.

［11］金挺, 孙智, 孙利民.裂缝扩展声发射有限元模拟及其信号时频分析［J］.振动、测试与诊断, 2007,27(4): 288-291.
JIN Ting, SUN Zhi, SUN Limin.Modeling of acoustic emission due to crack growth with finite element method and time-frequency analysis of the signal［J］.Journal of Vibration, Measurement & Diagnosis, 2007,27(4): 288-291.

［12］闫维明,马裕超,何浩祥,等.双线性时频分布交叉项提取及损伤识别应用［J］.振动、测试与诊断, 2014,36: 1014-1022.
YAN Weiming, MA Yuchao, HE Haoxiang, et al.Cross-term extraction of bilinear time-frequency distribution and application of damage identification［J］.Journal of Vibration, Measurement & Diagnosis, 2014,36: 1014-1022.

［13］程发斌, 汤宝平, 刘文艺.一种抑制维格纳分布交叉项的方法及在故障诊断中应用［J］.中国机械工程, 2008,19(14): 1727-1731.
CHENG Fabin, TANG Baoping, LIU Wenyi.A method to suppress cross-terms of wigner-ville distribution and its application in fault diagnosis［J］.China Mechanical Engineering, 2008,19(14): 1727-1731.

［14］蔡艳平,李艾华,王涛,等.基于EMD-Wigner-Ville的内燃机振动时频分析［J］.振动工程学报, 2010,23(4): 430-437.
CAI Yanping, LI Aihua, WANG Tao, et al.I.C.engine vibration time-frequency analysis based on EMD Wigner-Ville［J］.Journal of Vibration Engineering, 2010,23(4): 430-437.

［15］何星, 王宏力, 姜伟, 等.改进的自适应EEMD方法及其应用［J］.系统仿真学报, 2014,26(4): 869-873.
HE Xing, WANG Hongli, JIANG Wei, et al.Improved adaptive EEMD method and its application［J］.Journal of System Simulation, 2014,26(4): 869-873.

［16］齐咏生, 白宇, 高胜利, 等.基于AVMD和谱相关分析的风电机组轴承故障诊断［J］.太阳能学报, 2019,40(7): 2053-2063.
QI Yongsheng, BAI Yu, GAO Shengli, et al.Fault diagnosis of wind turbine bearing based on AVMD and spectral correlation analysis［J］.Acta Enerciae Solaris Sinica, 2019,40(7): 2053-2063.

［17］刘长良，武英杰，甄成刚.基于变分模态分解和模糊C均值聚类的滚动轴承故障诊断［J］.中国电机工程学报, 2015,35(13): 3358-3365.
LIU Changliang, WU Yingjie, ZHEN Chenggang.Rolling hearing fault diagnosis haled on variational mode decomposition and fuzzy C means clustering［J］.Proceedings of the CSEE, 2015,35(13): 3358-3365.

［18］CORBETTA M, SAXENA A, GIGLIO M, et al.An investigation of strain energy release rate models for real-time prognosis of fiber-reinforced laminates［J］.Composite Structures, 2017,165: 99-114.

［19］WU Fuqiang, YAO Weixing.A fatigue damage model of composite materials［J］.International Journal of Fatigue, 2010,32: 134-138.

［20］VIEILLE B, CHABCHOUB M, BOUSCARRAT D, et al.A fracture mechanics approach using Acoustic Emission Technique to investigate damage evolution in woven-ply thermoplastic structures at temperatures higher than glass transition temperature［J］.Composites Part B, 2017,116: 340-351.

［21］ANEESH C, KUMAR S, HISHAM P M, et al.Performance comparison of variational mode decomposition over empirical wavelet transform for the classification of power quality disturbances using support vector machine［J］.Procedia Computer Science, 2015,46: 372-380.

［22］ZHOU Wei, ZHAO Wenzheng, ZHANG Yannan, et al.Cluster analysis of acoustic emission signals and deformation measurement for delaminated glass fiber epoxy composites［J］.Composite Structures, 2018,195: 349-458.

［23］陈思妤.基于时频分析的水声信号盲分离方法研究［D］.哈尔滨：哈尔滨工业大学,2016.

［24］KUMAR C, ARUMUGAM V, SANTULLI C.Characterization of indentation damage resistance of hybrid composite laminates using acoustic emission monitoring［J］.Composites Part B-Engineering, 2017,111: 165-175.