基于小波分析和SVM的控制图模式识别

Control Chart Pattern Recognition Based on Wavelet Analysis and SVM

为提高控制图模式尤其是混合控制图模式的识别精度,提出了基于小波分析和支持向量机(SVM)的控制图模式识别方法。该方法通过对工序质量特征数据进行小波包分解,提取低频逼近序列和各频带能量信息,并以此作为SVM分类器的输入,分别识别控制图模式中的趋势信号、阶跃信号和周期信号,最后通过合并这些信号以确定控制图的模式。通过仿真实验的验证,表明该方法相比传统的控制图模式识别方法,具有较好的识别精度。

Control chart pattern recognition based on wavelet analysis and SVM was presented to improve the accuracy of control chart pattern recognition,in particular the accuracy of hybrid control pattern recognition.Process quality characteristics data were decomposed in wavelet packet to extract low-frequency approximation signals and the band energy information,which were taken as the inputs of SVM classifier recognizing trends,step and periodic signals respectively,and these signals figure the final model together.The simulation experiment used for testing recognition effect shows that the method based on wavelet analysis and SVM has an advantage over traditional methods in identification accuracy.