基于奇异值分解和灰靶决策的车刀磨损状态判别

Wear Condition Recognition of Lathe Tool Based on Singular Value Decomposition and Grey Target Decision Methods

针对随机噪声干扰车刀磨损振动信号时域特征提取,车刀磨损判别精度不高的问题,提出一种通过小波包变换和相关系数法提取车刀振动信号的磨损时域特征,采用奇异值分解对磨损时域特征进行去噪处理,去噪处理后获取磨损时域特征。选取与车刀磨损最相关的磨损特征作为参考特征序列,计算参考特征序列与其余磨损特征序列之间的相似关联度,对相似关联度归一化处理得到各磨损时域特征的权值,使用灰靶决策计算各磨损时域特征的综合测度,确定车刀磨损状态。实验结果表明:该方法可以有效地滤除随机噪声干扰。

In allusion to the problems that random noise interferes the time domain vibration signal extraction, and limited lathe tool wear condition recognition accuracy. This study proposed a time domain characteristics extraction method of tool wear signals which firstly extracts the time domain characteristics of the lathe tool vibration signal with wavelet packet transform and correlation coefficient methods, then denoises the time domain characteristics with singular value decomposition（SVD） method. The most relevant wear characteristics of lathe tool weariness are selected as reference characteristic sequence to calculate the similarity association degrees with other wear characteristic sequences. Then the weight of each time domain wear characteristics are gain through normalization process of similarity association degrees, and the comprehensive measurement of time domain characteristic of each wear signal are calculated with grey target decision method in order to determine the tool wear condition of the lathe tool. Experiment results indicate that the proposed method can filter the random noise effectively.