基于连续小波和多类球支持向量机的颤振预报  

Chatter Prediction Based on Continuous Wavelet Features and Multi-class Spherical Support Vector Machine

作　　者：吴石[1] 刘献礼[1] 王艳鑫[1] 

机构地区：[1]哈尔滨理工大学机械动力工程学院,哈尔滨150080

出　　处：《振动．测试与诊断》2012年第1期46-50,160,共5页Journal of Vibration,Measurement & Diagnosis

基　　金：国家科技重大专项子课题资助项目(编号:2009ZX04014-066-03);国家自然科学基金资助项目(编号:50875068);中国博士后基金资助项目(编号:20110491098)

摘　　要：研究了一种应用连续小波特征和多类球支持向量机进行铣削系统颤振预报的方法,该方法基于连续小波变换提取铣削振动信号的特征,利用多类球支持向量机对正常铣削状态、颤振孕育状态和颤振爆发状态的振动信号进行三分类识别,通过识别颤振孕育状态预测颤振爆发。试验结果表明,在铣削颤振识别与预测中,铣削振动信号的连续小波特征与多类球支持向量机相结合具有良好的识别颤振孕育状态和颤振爆发状态的能力,颤振孕育状态的识别正确率达95.0%,颤振爆发状态的识别正确率达97.5%。A method of chatter forecast is studied by the application of continuous wavelet feature vector and support vector machine(SVM) for ball milling system.This method is based on continuous wavelet transformation to extract feature vector of milling vibration signal and multi-class spherical support vector machine is used to classify three classification and recognition such as normal milling state,chatter gestation state and chatter outbreak of state,which predicts the chatter outbreak by making a recognition of chatter gestation state.As experimental results show,there is a good ability to identify and forecast chatter in recognition and predict of milling vibration,which uses continuous wavelet feature vector and multi-class spherical SVM classifier to deal with milling vibration signal,recognition rate of chatter gestation state reaches 95.0%,then chatter outbreak state recognition rate is 97.5%.

关 键 词：颤振预报 颤振孕育 连续小波 球形支持向量机 多类支持向量机 

分 类 号：TG506.1[金属学及工艺—金属切削加工及机床] TP391.4[自动化与计算机技术—计算机应用技术]