摘要
在刀具磨损状态监测中,能够提取到的反映不同刀具磨损状态的特征量较大,基于神经网络的状态识别无法去掉冗余特征,会存在训练时间长和准确率降低等问题。针对这些问题,提出基于粗糙集-BP神经网络的刀具磨损状态监测方法,利用粗糙集对特征进行属性约简,去掉冗余信息,从而优化特征,并且减少神经网络的输入端数据,可以缩短神经网络的训练时间和提高识别的准确率。通过对实测刀具数据进行分析,证明了该方法的有效性。
In the tool wear condition monitoring,a large number of features reflecting different tool wear states can be extracted.Based on the features of state recognition based on Neural Network unable to remove redundant,the problems of long training time and low accuracy were caused. A tool wear condition monitoring method based on rough set and BP Neural Network was presented by aimed at these problems. By using rough set to reduce attributes,to remove redundant information,and then optimize features were optimized. Moreover the input data of neural network were reduced,and then the training time of neural network was shortened and recognition accuracy was improved. The effectiveness of this method has been proved through the analysis of the tool data from practical monitoring.
出处
《机床与液压》
北大核心
2015年第5期49-52,共4页
Machine Tool & Hydraulics
基金
中央高校基本科研业务费专项基金资助项目(SWJTU12CX039)
关键词
刀具状态监测
粗糙集理论
BP神经网络
小波包分析
Tool condition monitoring
Rough set theory
BP Neural Network
Wavelet packet analysis
