基于多特征融合的刀具磨损识别方法

针对刀具磨损监测中多传感器融合监测方法的缺点，提出了基于声发射信号多特征融合与最小二乘支持向量机(lease square support vector machine,简称LS-SVM)相结合的刀具磨损状态监测方法。首先，分别采用经验模态分解法、双谱分析法以及小波包分析法提取采样信号在时域、频域、时-频域内的特征，构造联合多特征向量；然后，利用核主元分析法(kernel principal component analysis,简称KPCA)对联合多特征向量进行融合降维处理，通过提取累积贡献率大于85%的主元，剔除了联合多特征中与刀具磨损相关性较小的冗余特征，生成融合特征；最后，将融合特征送入最小二乘支持向量机，有效地实现了（尤其在小样本下）刀具磨损状态的识别，与神经网络识别方法相比具有更高的识别率。

Identification Method of Tool Wear Based on Multi-Feature Fusion

Considering the deficiency in the multi-sensor fusion method for cutting-tool wear monitoring, a method using multi-feature fusion and least squares support vector machines based on acoustic emission signals is put forth. First, by method of empirical mode decomposition, bispectral analysis and wavelet packet analysis, the feature of sampling signals in domain of time, frequency and time-frequency is extracted to construct a multi-feature vector. Its dimension is then reduced using kernel principal component analysis. The fusion feature is generated by extracting the principal component whose cumulative contribution rate is above 85% and rejecting the redundant feature that has a lower correlation to cutting-tool wear. Finally, the fusion feature is put into least squares support vector machines. This method can effectively recognize the cutting-tool wear condition, especially in small samples, and has higher recognition rates than that of a neural network.