基于磨削声与电流的砂带磨损状态识别  

Wear Identification of Abrasive Belt Based on Grinding Sound and Current

作　　者：程灿 李建勇[1,2] 刘月明 聂蒙[1,2] CHENG Can;LI Jianyong;LIU Yueming;NIE Meng(School of Mechanical,Electronic and Control Engineering,Beijing Jiaotong University Beijing,100044,China;Key Laboratory of Vehicle Advanced Manufacturing,Measuring and Control Technology,Ministry of Education Beijing,100044,China)

机构地区：[1]北京交通大学机械与电子控制工程学院,北京100044 [2]载运工具先进制造与测控技术教育部重点实验室,北京100044

出　　处：《振动．测试与诊断》2020年第2期355-360,422,共7页Journal of Vibration,Measurement & Diagnosis

基　　金：国家自然科学基金资助项目(51605024)。

摘　　要：为实时监测砂带磨损状态,采用基于磨削声信号与电流信号的监测方案。首先,利用时域分析方法与小波包分析方法提取砂带磨损信号特征,通过朴素贝叶斯方法融合两种信号,从而识别砂带磨损状态;其次,为提高砂带磨损状态识别准确率,针对朴素贝叶斯方法的分类特性,改进了一种基于Fisher判别率与互信息的信号特征选择方法。实验结果表明,利用基于Fisher判别率与互信息方法能够挑选出可分性好同时特征间相关性弱的信号特征,基于朴素贝叶斯的砂带磨损状态识别方法能够准确地识别砂带磨损状态。The wear of abrasive belt is monitored based on the grinding sound and current. First, the feature of these signalsare extracted by the time domain analysis method and the wavelet packet analysis method, and merged by Naive Bayes classifier to identify the abrasive belt. Furthermore, to increase the accuracy of identification, the Naive Bayes classifieris improved by introducing a signal feature selection method based on Fisher discriminant rate and mutual information. The experimental results show that the signal features with good separability and weak correlation can be selected by the improved feature selection method, and the wear state recognition method based on Naive Bayes classifier can identify the abrasive wear state accurately.

关 键 词：砂带磨损 朴素贝叶斯 状态识别 声信号 电流信号 

分 类 号：TG74[金属学及工艺—刀具与模具] TH117[机械工程—机械设计及理论]