摘要
针对刀具磨损声发射信号的非平稳特征,提出了基于奇异值分解和最小二乘支持向量机的刀具磨损状态识别方法。该方法首先对声发射信号进行经验模态分解,将其分解为若干个固有模态函数之和,利用固有模态函数构造初始特征向量矩阵,然后对初始特征向量矩阵进行奇异值分解,计算奇异谱,将奇异谱做为特征向量,送入最小二乘支持向量机训练、识别。结果表明:该方法能很好地识别刀具磨损状态,与神经网络相比具有更高的识别率。
In view of the non-stationary characteristics of acoustic emission signal of tool wear, a novel method of tool wear state identification based on Singular value decomposition and Least Squares Support Vector Machine is proposed. Firstly, the Empirical Mode Decomposition method is used to decompose the collected acoustic emission signals into a number of Intrinsic Mode Functions which is used to construct the initial feature vector matrix. Then by applying the singular value decomposition method to the initial feature vector matrix, the singular values are obtained. Finally, the singular spectrum are picked up to constitute the feature vector. The feature vector is put into Least squares support vector machine to train and identify the tool wear state. The identification result proves that this method is superior to neural network, and it has a higher identification rate. Result also proves that this method is efficient and feasible.
出处
《东北电力大学学报》
2013年第3期5-9,共5页
Journal of Northeast Electric Power University
基金
东北电力大学博士科研启动基金资助(BSJXM-201115)
关键词
刀具磨损状态识别
奇异值分解
经验模态分解
最小二乘支持向量机
Tool wear condition monitoring
Singular value decomposition
Empirical mode decomposition
Least squares support vector machine
