复杂背景噪声下风机叶片裂纹故障声学特征提取方法

针对大型风机叶片裂纹故障声学诊断问题,提出一种非接触式的叶片状态远程在线声学监测系统,给出了叶片裂纹故障的声学特征自适应提取方法.首先设计了面向复杂环境噪声的原始声信号预处理算法,然后采用1/6倍频程粗略刻画叶片声信号的频谱总体变化趋势,提取无量纲的倍频程能量比构造支持向量机分类器的输入特征向量,最后引入主成分分析法自适应的优化高维特征空间.风场实测数据验证了该算法的有效性.

Acoustical Crack Feature Extraction of Turbine Blades under Complex Background Noise

To solve the problem of crack detection of large turbine blades, the author proposed a non-contact online acoustic health monitoring system and studied in-depth on the adaptive crack feature ex-traction method. Firstly, a preprocessing algorithm is well designed to remove the complex background noise. Then 1/6 octave technique is used to reveal the spectrum change of acoustic signal roughly, and concluded that the octave energy ratios are extracted as input feature vector of the support vector machine classifier. Finally, the principal component analysis is introduced to optimize the high dimensional feature space adaptively. The measured data from wind field validates the effectiveness of proposed method.