刀具磨损特征参数提取与状态识别方法

Cutting Tool Wear Characteristic Parameters Extraction and Wear State Recognition

为提高刀具磨损状态识别准确率,文章提出了S变换时频图纹理特征参数提取方法和基于隐马尔科夫模型的磨损状态识别方法。以声发射信号为敏感信号,设计了刀具磨损实验方案;基于EEMD算法,提出了互相关系数与鞘度相结合的综合降噪方法;使用S变换处理声发射信号得到等高线灰度图,通过灰度共生矩阵提取等高线灰度图的纹理特征参数;将类内散布矩阵和类间散布矩阵结合,提出了基于散布矩阵的特征参数敏感度分析和降维方法;采用基于隐马尔科夫模型的磨损状态识别方法,分别将全维特征参数和降维特征参数用于磨损状态识别,实验结果表明,全维特征参数的识别准确率为88.34%,降维特征参数的识别准确率为100%。

To improve recognition accuracy of cutting tool wear state,s-transform time-frequency graph texture feature parameters extraction method and wear state recognition method based on hidden Markov model are proposed.Acoustic emission signal as sensitive signal,cutting tool wear experimental scheme is designed.based on EEMD algorithm,comprehensive denoise method combined cross correlation coefficients and sheath degree is put forward.Contour line grey-scale image of acoustic emission signal is gotten by S-transform,and textual feature parameters of contour line grey-scale image is calculated by grey-level co-occurrence matrix.Intra-class scattering matrix and inter-class scattering matrix are combined,so that characteristic parameters sensitivity analysis method and dimensionality reduction method is raised.Hidden Markov model is employed to recognize wear state.Full-dimensional characteristic parameters and reduced--dimensional characteristic parameters are used to recognize wear state,and the experiment shows that accuracy rate by full-dimensional characteristic parameters is 88.34%,and 100% by reduced-dimensional characteristic parameters.