基于云理论与LS-SVM的刀具磨损识别方法

Method of Tool Wear Recognition Based on Cloud Theory and LS-SVM

针对刀具磨损过程中产生声发射信号的不确定性以及神经网络学习算法收敛速度慢、易陷入局部极小值、对特征要求较高等问题,提出了基于云理论和最小二乘支持向量机的刀具磨损状态识别方法。首先,对声发射信号进行小波包分解与重构,滤除干扰频段对求取特征参数的影响;其次,对重构后的信号利用逆向云算法提取云特征参数:期望、熵、超熵,分析刀具磨损声发射信号的云特性及磨损状态与云特征参数之间的关系;最后,将云特征参数组成特征向量送入最小二乘支持向量机进行识别。研究结果表明:所提取的特征可以很好地反映刀具的磨损状态,云-支持向量机方法可以有效地实现刀具磨损状态的识别,与传统神经网络识别方法相比具有更高的识别率,识别率达到96.67%。

Aiming at the uncertainty of the tool wear acoustic emission signal and slow convergence speed,easy to fall into local minimum value,and higher feature requirements of the neural network learning algorithm,the method for tool wear state recognition is put forward based on cloud theory and least squaressupport vector machine(LS-SVM).First of all,the acoustic emission signal is decomposed and reconstructed by wavelet packet,filtering out the influence of interference spectrum for calculating characteristic parameters;Secondly,reverse cloud algorithm is used for extracting the cloud characteristics parameters:expectations,entropy and hyper entropy from the reconstruction signal,and analyzed tool wear AE signal characteristics parameters of cloud and the relationship between wear and the characteristic of cloud;Finally the cloud characteristic parameters of feature vector are put into the least squares support vector machine to recognize the state of tool wear.Research results show that the extracted features could reflect the state of tool wear,cloud-LS-SVM method can realize the tool wear state recognition.Compared with the traditional neural network recognition method,cloud-LS-SVM method has a higher recognition rate,and the recognition rate is 96.67%.