风力机叶片疲劳裂纹AE信号的小波变换优化方法
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摘要
为实现风力机叶片的及时有效地监测和维护,使用声发射技术采集疲劳裂纹信号,从而提取裂纹特征。而声发射信号的突发性和冲击性需要具有时频分析能力的信号处理方式来提纯和降噪,小波变换方法作为常用的时频处理方式油漆有效性,但是现有的小波基函数不足以适应该信号的分析。提出基于Shannon熵理论计算疲劳裂纹扩展的声发射信号的小波基函数带宽参数,得到最适合此裂纹声发射信号的Morlet小波基函数,计算优化基函数的小波,获得风力机疲劳裂纹特征成分在时间尺度平面的高幅值能量分布。实验研究表明,优化小波基的方法具有很好的时频聚集性和抗噪能力,实现了风力机叶片裂纹声发射信号的时频特征清晰准确的提取。
In order to monitor and maintain fiber composite blades,acoustic emission(AE) techniques are employed to monitor fatigue crack in blades,and the feature of cracks is extracted.Given the abruptness and impact of AE signal,wavelet transformation method is commonly used as the time-frequency method effectively,but the existing wavelet basis is not enough to adapt to the AE signal of wind turbine blades.The method of optimization wavelet transform of AE signals is put forward.Basis function bandwidth of wavelet is calculated based on Shannon entropy,the most suitable basis function for AE signals of cracks of wind turbine blades.Therefore,the optimization wavelet of two types of crack AE signal has high amplitude energy distribution in time-scale plane.Experimental research proves that the proposed method has excellent timefrequency concentration and noise restraining ability,and extracts time-frequency fault feature of wind turbine blade AE signals distinctly.Moreover,this method can be applied for identification cracks and monitor the degraded condition in complex environment of wind turbine blades.
In order to monitor and maintain fiber composite blades,acoustic emission(AE) techniques are employed to monitor fatigue crack in blades,and the feature of cracks is extracted.Given the abruptness and impact of AE signal,wavelet transformation method is commonly used as the time-frequency method effectively,but the existing wavelet basis is not enough to adapt to the AE signal of wind turbine blades.The method of optimization wavelet transform of AE signals is put forward.Basis function bandwidth of wavelet is calculated based on Shannon entropy,the most suitable basis function for AE signals of cracks of wind turbine blades.Therefore,the optimization wavelet of two types of crack AE signal has high amplitude energy distribution in time-scale plane.Experimental research proves that the proposed method has excellent timefrequency concentration and noise restraining ability,and extracts time-frequency fault feature of wind turbine blade AE signals distinctly.Moreover,this method can be applied for identification cracks and monitor the degraded condition in complex environment of wind turbine blades.
引文
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[11]王更峰.基于小波包分析的岩石声发射测量地应力[J].岩土工程技术,2006,20(4):166-169.(Wang Geng-feng.In-situ geostress test by rock acoustic emission based on wavelt packet analysis[J].Geotechnical Engineering Technique,2006,20(4):166-169.)
[12]汤宝平,蒋永华,董绍江.重分配小波尺度谱的时频分布优化方法研究[J].仪器仪表学报,2010,31(6):1330-1334.(Tang Bao-ping,Jiang Yong-hua,Dong Shao-jiang.Time-frequency representation optimization of reassigned wavelet scalogram[J].Chinese Journal of Scientific Instrument,2010,31(6):1330-1334.)
[13]何正嘉,訾艳阳,孟庆风.机械设备非平稳信号的故障诊断原理及应用[M].北京:高等教育出版社,2001.(He Zheng-jia,Zi Yan-yang,Meng Qing-feng.Fault Diagnosis Principle and Application of Non-Stationary Signal of Mechanical Equipmen[tM].Beijing:Higher Education Press,2001).
[2]J.C.Marín,A.Barroso,F.París,J.Ca?as.Study of fatigue damage in wind turbine blades[J].Engineering Failure Analysis,2009,16(2):656-668.
[3]H.L.Dunegan.Detection of fatigue crack growth by acoustic emission technique[sJ].Materials Evaluation.1970,28(10):221-223.
[4]Ziola S.Digital signal processing of modal emission signals[J].Journal of Acoustic Emission,1996,14(3/4):12-18.
[5]LekouD,VionisP,JoossePA.Full-scalebladetesting enhanced byacoustic emission monitoring[C].Proc European Wind Energy Conference,Madrid,Spain,2003.
[6]Paquette J,Van Dam J,Hughes S.Structural testing of 9m carbon fiber wind turbine research blades[C].AIAA 2007 Wind Energy Symposium,Reno,USA,2007.
[7]Tavner P J.Review of condition monitoring of rotating electrical machines[J].IETElectricPowerApplica-tions,2008,2(4):215-247.
[8]朱忠奎,陈再良,王传洋.基于小波尺度图重分配的轴承瞬态特征检测与提取[J].数据采集与处理,2005,20(3):356-360(Zhu Zhong-liang,Chen Zai-liang,Wang Chuan-yang.Bearing transient feature detection and extraction based on reassigned scalogram[J].Journal of Data Acquisition&Processing,2005,20(3):356-360)
[9]宁辰校.小波分析及其在机械故障诊断中的应用[J].机械设计与制造,2002(2):5-6.(Ning Chen-xiao.Study of signal processing in machinery diagnosis[J].Machinery Design&Manufacture,2002(2):5-6).
[10]陈顺云,杨润海.小波分析在声发射资料处理中的初步应用[J].地震研究,2002,25(4):328-334.(ChenShun-yun,YangRun-hai.Thepreliminaryapplication of wavelet analysis to the data process of acoustic emission[J].Journal of seismological research,2002,25(4):328-334.)
[11]王更峰.基于小波包分析的岩石声发射测量地应力[J].岩土工程技术,2006,20(4):166-169.(Wang Geng-feng.In-situ geostress test by rock acoustic emission based on wavelt packet analysis[J].Geotechnical Engineering Technique,2006,20(4):166-169.)
[12]汤宝平,蒋永华,董绍江.重分配小波尺度谱的时频分布优化方法研究[J].仪器仪表学报,2010,31(6):1330-1334.(Tang Bao-ping,Jiang Yong-hua,Dong Shao-jiang.Time-frequency representation optimization of reassigned wavelet scalogram[J].Chinese Journal of Scientific Instrument,2010,31(6):1330-1334.)
[13]何正嘉,訾艳阳,孟庆风.机械设备非平稳信号的故障诊断原理及应用[M].北京:高等教育出版社,2001.(He Zheng-jia,Zi Yan-yang,Meng Qing-feng.Fault Diagnosis Principle and Application of Non-Stationary Signal of Mechanical Equipmen[tM].Beijing:Higher Education Press,2001).
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