风电机组叶片裂纹故障特征提取方法

Study on Extracting Crack Fault Feature of Wind Turbine Blades

为实现风电机组叶片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,从而提取不同裂纹特征。鉴于尺度谱受到Heisenberg测不准原理的极限制约,时频聚集性不佳、干扰强的现象,提出风电机组叶片裂纹声发射信号的优化小波重分配尺度谱分析。基于Shannon熵理论计算裂纹萌生和预制裂纹再扩展的声发射信号的重分配尺度谱小波基函数带宽参数,得到最适合此两阶段裂纹声发射信号的Morlet小波基函数,计算优化基函数的小波重分配尺度谱,获得不同类型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,优化小波重分配尺度谱的方法具有很好的时频聚集性和抗噪能力,实现了风电机组叶片裂纹声发射信号的时频特征清晰准确的提取,识别风电机组叶片不同阶段裂纹故障。进而可以采用该方法监测风电机组叶片在复杂环境中的退化状态。

In order to monitor and maintain fiber composite blades, acoustic emission techniques were employed to monitor fatigue crack in blades, and the feature of different cracks was extracted. Given the limit of Heisenberg-Gabor inequality, the wavelet scalogram has poor frequency, time concentration and strong interference. The method of optimization reassigned wavelet scalograms of AE signals was put forward. Basis function bandwidth of reassigned wavelet scalogram was calculated based on Shannon entropy. The most suitable basis function for AE signals of propagation cracks and initiation cracks was attained. Therefore, the optimization reassigned wavelet scalogram 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 time-frequency concentration and noise restraining ability, and extract 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.