摘要
为了在强大噪声干扰下提取风力机叶片的早期裂纹特征,识别不同种类的裂纹,通过搭建声发射设备检测风力机叶片复合材料块实验平台,采集扩展裂纹与萌生裂纹的声发射信号,并借助小波尺度谱优越的时频分析性能来有效提取裂纹信号的特征,以区别扩展裂纹和萌生裂纹.实验结果表明,小波尺度谱能有效提取非线性、非平稳信号中的故障特征,优于小波分析方法.通过实验研究,得到了识别扩展裂纹和萌生裂纹的判据,建立了基于声发射和小波尺度谱的风力机叶片裂纹识别新方法.
In order to extract the early crack feature of wind turbine blades with strong noise interference and identify different kinds of cracks,the experimental platform was constructed to detect the composite material block of wind turbine blades,and the acoustic emission（AE） signals of both propagated and initiated cracks were collected.With the superior time-frequency analysis performance of wavelet scalogram,the signal characteristics of cracks were extracted effectively,and the propagated and initiated cracks were distinguished.The experimental results reveal that the wavelet scalogram identification can extract the fault feature in nonlinear and non-stationary signals effectively,and is better than the wavelet analysis method.The criterion for recognizing the propagated and initiated cracks was obtained with the experiments and research.The new method for the crack identification of wind turbine blades based on AE and wavelet scalogram was established.
出处
《沈阳工业大学学报》
EI
CAS
北大核心
2012年第1期22-25,47,共5页
Journal of Shenyang University of Technology
基金
国家自然科学基金资助项目(50975180
51005159)
关键词
风力机
叶片
裂纹
声发射
小波尺度谱
复合材料
早期损伤识别
信号提取
wind turbine
blade
crack
acoustic emission
wavelet scalogram
composite material
early damage identification
signal extraction
