基于核主成分分析及支持向量机的水轮机叶片裂纹源定位

Source location of cracks in a turbine blade based on kernel principal component analysis and support vector machines

结合核主成分分析(KPCA)以及支持向量机对水轮机转轮叶片裂纹源的声发射信号进行定位。结果表明,利用核主成分分析提取的特征参数进行定位的精度高于原始参数的定位精度,即输入9个特征参数时,支持向量机在叶片区域的识别率为100%,在裂纹源对焊缝距离的支持向量回归分析中的最大误差为20cm。因而结合KPCA和支持向量机对复杂的大尺寸结构进行定位是一种较好的方法,既减少了输入信号的维数,又提高了定位精度。

Source location of acoustic emission signals of a crack based on kernel principal component analysis (KPCA) and support vector machines (SVM) was studied here.The results showed that the accuracy of location using the feature parameters extracted with KPCA technique is improved comparing with that using the original parameters,i.e.,the recognition rate of the crack region is 100 percent; the maximum error of the support vector regression analysis for the distance from the source of cracks to the welding seam is 20cm when the number of the input feature parameters is nine.As a result,it was a good method to combine KPCA with SVM for crack source location of complex big-size structures.It decreased the dimensions of input signals and improved the accuracy of location as well.