基于EMD分量与小波包能量熵的轧辊磨削颤振在线预测  

On line prediction of roll grinding chatter based on EMD component and wavelet packet energy entropy

作　　者：朱欢欢 迟玉伦[2] 张梦梦 熊力[2] 应晓昂 ZHU Huanhuan;CHI Yulun;ZHANG Mengmeng;XIONG Li;YING Xiaoang(Department of Manufacturing Shanghai Technician School,Shanghai 200437,China;School of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;Higher Vocational College,Shanghai University of Engineering Science,Shanghai 200437,China)

机构地区：[1]上海市高级技工学校制造工程系,上海200437 [2]上海理工大学机械工程学院,上海200093 [3]上海工程技术大学高职学院,上海200437

出　　处：《金刚石与磨料磨具工程》2024年第1期73-84,共12页Diamond & Abrasives Engineering

摘　　要：针对轧辊磨削颤振时的时频域单一处理方法存在部分特征丢失的问题,提出了时频域相结合的方法对信号进行特征处理,并利用智能算法实现轧辊磨削颤振的在线预测。首先,利用经验模态分解(empirical mode decomposition,EMD)方法对振动传感器信号进行分解获得各固有模态函数(intrinsic mode function,IMF),剔除“虚假分量”后计算表征轧辊磨削颤振的时域特征。然后,利用小波包能量熵对声发射传感器信号求解频率段节点能量熵值,获得表征轧辊磨削颤振的频域特征。最后,将上述时频域特征降维后代入智能算法模型实现对轧辊磨削加工的在线预测。结果表明:LV-SVM模型的磨削颤振分类平均准确率达92.75%,模型平均响应时间为0.7765 s;验证了时频域特性的EMD和小波包能量熵方法的LV-SVM在线预测轧辊磨削颤振的有效性。To address the issue of partial feature loss in the single processing method within the time-frequency domain for roll grinding chatter,a combined time-frequency domain method is proposed to process signal feature.An intelligent algorithm is used to achieve online prediction of roll grinding chatter.Firstly,the empirical mode decomposition(EMD)method is utilized to decompose the vibration sensor signals,extrating the intrinsic mode function(IMF)while removing"spurious components"to calculate time domain characteristics associated with roll grinding chatter.Then,wavelet packet energy entropy is used to solve the frequency band node energy entropy values of acoustic emission sensor signals,obtaining frequency domain features characterizing the roll grinding chatter.Finally,the time-frequency domain features after dimension reduction is substituted into the intelligent algorithm model for online prediction of the roller grinding process.The results show that the the LV-SVM model achieves an average classification accuracy of 92.75%,with an average response time of 0.7765 s.This verifies the validity of EMD and LV-SVM based on wavelet packet energy entropy in the time-frequency domain for online prediction of roller grinding chatter.

关 键 词：轧辊磨削颤振 EMD分解 固有模态函数 小波包能量熵 最小二乘支持向量机 

分 类 号：TG58[金属学及工艺—金属切削加工及机床]