摘要： 针对摩擦振动和噪声信号较难获得、信号抗干扰能力差以及生产中难以得到大量摩损状态样本的情况,提出小波包熵和支持向量机(SVM)相结合的机床导轨摩擦磨损状态识别方法。该方法通过小波包分解方法将信号分解到独立相邻的节点频带中,设计对比实验获得导轨摩擦信息特征频带对应的小波包节点序列,以该序列小波包能量熵值建立特征向量作为SVM的输入参数。实验结果表明,以多项式核函数和径向基核函数建立的SVM分类器平均识别率分别达到72.2%和83.3%,具有较好的预测推广能力及较高的识别准确率。

关键词: 摩擦振动, 信号处理, 小波包分解, 支持向量机, 状态识别

Abstract: The frictional vibration and noise signal are difficult to obtain,signal anti-interference ability is poor,and a large number of samples in the production are difficult to obtain.In view of this a scheme for recognition of friction and wear state of machine tool guide based on wavelet package entropy and Support Vector Machine(SVM) is put forward in this paper.Signal is decomposed into independent adjacent node band by wavelet packet decomposition.Comparative experiment is designed to obtain wavelet packet node sequences corresponding to the guide rail friction characteristic frequency band.The feature vectors are established by the sequence of wavelet packet energy entropy which are as the input parameters of the SVM.Experimental results show that the average recognition rates of the SVM classifier which is established through polynomial kernel function and radial basis kernel function can reach 72.2% and 83.3% separately,which has good prediction generalization ability and high recognition accuracy.

Key words: frictional vibration, signal processing, wavelet packet decomposition, Support Vector Machine(SVM), state recognition

中图分类号: 

• TH166