基于小波奇异性和支持向量机微铣刀破损检测

针对微铣削过程中刀刃破损的现象,提出了基于振动信号奇异性分析的自学习式支持向量机的刀具破损检测方法.对两种状态信号作连续小波变换,计算小波模极大值和信号的李普希兹指数(Lips).通过Lips识别刀具状态,拟合Lips分布概率密度函数并验证其符合正态分布,将Lips分布的均值、方差作为特征值,通过遗传算法参数寻优建立了刀具破损状态的支持向量机(SVM)识别模型,也称最优模型.利用最优模型预测刀具破损状态,其预测准确度从84%逐步提高至90%,提升了系统预测模型的鲁棒性.最后通过实验验证了该方法的有效性.

Micro Milling Cutter Breakage Detection Based on Wavelet Singularity and Support Vector Machine

A tool breakage detection method was proposed based on the singularity analysis of vibration signal and self-learning support vector machine. The measured vibration signals were decomposed by the continuous wavelet transform， the wavelet modulus maxima (WTMM) and the Lipschitz index (Lips) were calculated. The state of tool breakage was recognized by Lips， and the Lips probability density function was fitted， which obeys the normal distribution. The support vector machine identification model of tool state was established by the parameter optimization of genetic algorithm based on mean value and variance of Lips (also called the optimal model). The tool breakage state was predicted by using this model， of which prediction accuracy increased gradually from 84% to 90%， and the robust of system prediction model was improved. Finally， the effectiveness of this method was verified by the experiments.