CAJ | 학술논문

为了检测车削过程中的颤振，提出一种颤振在线智能检测方法。使用最小二乘一类支持向量机，训练出描述特征矢量集的超球面，通过计算被测样本与超球面的距离来判断其是否颤振。基于相干准则和分块矩阵求逆，构造了在线稀疏结构的最小二乘一类支持向量机，将特征信息存储于特征库(字典)中，通过更新特征库实现检测模型的在线进化。在颤振检测的应用中，首先使用小波包分解，得到第三层节点能量的比例作为特征矢量，以离线数据构造特征矢量作为输入，训练得到初始检测模型以及特征库，在线检测中不断更新特征库，实现检测模型的在线进化。试验结果表明，在车削颤振识别中，在线进化的检测模型的识别效果更好，颤振预报准确率高达至99.04%，优于离线模型的预报准确率96.74%。
위료검측차삭과정중적전진，제출일충전진재선지능검측방법。사용최소이승일류지지향량궤，훈련출묘술특정시량집적초구면，통과계산피측양본여초구면적거리래판단기시부전진。기우상간준칙화분괴구진구역，구조료재선희소결구적최소이승일류지지향량궤，장특정신식존저우특정고(자전)중，통과경신특정고실현검측모형적재선진화。재전진검측적응용중，수선사용소파포분해，득도제삼층절점능량적비례작위특정시량，이리선수거구조특정시량작위수입，훈련득도초시검측모형이급특정고，재선검측중불단경신특정고，실현검측모형적재선진화。시험결과표명，재차삭전진식별중，재선진화적검측모형적식별효과경호，전진예보준학솔고체지99.04%，우우리선모형적예보준학솔96.74%。
In order to detect chatter in the process of turning, an online intelligent chatter detection method is proposed. In this method, least squares one class support vector machine(LS-OC-SVM) is used to extract a hyper plane as an optimal description of training objects. Chatter is detected by computing the distance between the sample to be tested and the hyper plane. Sparse online LS-OC-SVM is proposed based on coherence criterion and partitioned matrix inversion, so that features information can be stored in the feature library which is also called dictionary. The detection model can be evolved continuously through the online update of feature library. In the application of chatter detection, firstly, feature vector is constructed for chatter detection based on node energy ratios of the third level of wavelet packet decomposition. Then, initial detection model and feature library are trained by using offline feature vectors as input. In the online detection scheme, the detection model is evolved while feature library is updated. The experimental results show that the online evolution model performs better than offline model in the cutting chatter detection. Chatter detection accuracy of the online evolution model is 99.04%, which is better than offline model whose detection accuracy is 96.74%.