基于小波能谱系数的风力机疲劳裂纹特征  

Crack Fault Feature of Wind Turbine Blade Based on Wavelet Energy Spectrum Coefficient

作　　者：赵新光[1] 甘晓晔[1] 谷泉[1] 周勃 陈长征 

机构地区：[1]辽宁科技学院机械工程学院本溪,117004 [2]辽宁省振动噪声控制技术工程研究中心沈阳,110870

出　　处：《振动．测试与诊断》2014年第1期147-152,195-196,共6页Journal of Vibration,Measurement & Diagnosis

基　　金：国家自然科学基金资助项目(50975180,51005159);辽宁省自然科学基金资助项目(201102093)

摘　　要：提出了风力机叶片裂纹扩展声发射信号的优化小波重分配尺度谱及小波能谱系数相结合的分析法。基于Shannon熵理论计算裂纹扩展声发射信号的重分配尺度谱小波基函数带宽参数,得到最适合裂纹声发射信号的Morlet小波基函数。用优化后的小波基函数计算重分配尺度谱,获得裂纹扩展特征成分在时间尺度平面的高幅值能量分布,利用特征能谱系数表征其重分配尺度谱的特征。实验结果表明,该方法有良好的时频聚集性和抗噪能力,实现了风力机叶片裂纹扩展声发射信号的时频特征提取,得到了能谱系数作为特征向量表示信号特征。该方法可用来实现风力机叶片在复杂环境中的模式识别。Wind turbines are costly and difficult to repair in the field of new energy.Monitoring their health status and mastering the characteristic of blade fatigue crack propagation have been an important topic.A combining method of optimized reassigned scalogram and wavelet energy coefficient is presented to analyze wind acoustic emission(AE)signals of turbine blade crack propagation.Basis function bandwidth of reassigned wavelet scalogram is calculated based on Shannon entropy.The most suitable basis function for AE signals of propagation crack is attained.Therefore,the optimization reassigned wavelet scalogram of crack AE signal has high amplitude energy distribution in time-scale plane.Then,energy spectrum coefficient can be used to the optimization reassigned scalogram.Experimental research proves that the proposed method has excellent time-frequency concentration and noise restraining ability.It is achieved to extract the time-frequency characteristics of the wind turbine blade crack propagation of acoustic emission signals clearly.Energy spectrum coefficient as feature vector can show the characteristics of signals.Moreover,this method can be applied for real-time pattern recognition of blades in complex environments.

关 键 词：风力机叶片 重分配尺度谱 疲劳裂纹 特征能谱系数 

分 类 号：TH165[机械工程—机械制造及自动化] TK83[动力工程及工程热物理—流体机械及工程]