基于ICEEMDAN-RCMWPE与WOA-KELM数控机床刀具健康状态监测技术的研究  

Research of health monitoring technology for CNC machine tools based on ICEEMDAN-RCMWPE and WOA-KELM

作　　者：王寿元 李积元 张涛 WANG Shouyuan;LI Jiyuan;ZHANG Tao(College of Mechanical Engineering,Qinghai University,Xining 810016,CHN)

机构地区：[1]青海大学机械工程学院,青海西宁810016

出　　处：《制造技术与机床》2023年第9期52-59,共8页Manufacturing Technology & Machine Tool

摘　　要：针对数控机床刀具健康状态特征提取不足以及识别准确率低等问题,提出了一种刀具健康状态监测方法。首先,利用改进完全集合经验模态分解(ICEEMDAN)和精细复合多尺度加权排列熵(RCMWPE)进行信号处理与特征提取,提取出能够表征刀具健康状态的特征信息;其次经tSNE处理,实现特征信息的降维与融合;最后将获取的低维特征输入到构建的鲸鱼优化核极限学习机(WOA-KELM)健康状态识别模型,从而对刀具健康状态进行分类与识别。经实验验证表明,所提出的信号处理、特征提取以及状态识别模型在刀具健康状态监测方面取得了很好的成效,其状态识别准确率高达99.76%,能够高效、准确地对刀具磨损状态进行分类和识别。Aiming at the problems of insufficient extraction of tool health status features and low recognition accuracy of CNC machine tools,this paper proposes a tool health status monitoring method.Firstly,the improved complete set empirical mode decomposition(ICEEMDAN)and the fine composite multi-scale weighted permutation entropy(RCMWPE)are used for signal processing and feature extraction,and the feature information that can characterize the health state of the tool is extracted.Secondly,by tSNE processing,the dimensionality reduction and fusion of feature information are realized.Finally,the obtained low-dimensional features are input into the constructed whale-optimized kernel extreme learning machine(WOA-KELM)health recognition model,so as to classify and identify the tool health state.Experimental verification shows that the proposed signal processing,feature extraction and state recognition model have achieved good results in tool health monitoring,and its state recognition accuracy is as high as 99.76%,which can efficiently and accurately classify and identify tool wear status.

关 键 词：数控机床 刀具状态监测 ICEEMDAN RCMWPE 核极限学习机 

分 类 号：TH17[机械工程—机械制造及自动化]