基于可听声信号熵特征的刀具磨损状态监测  

Tool wear condition monitoring based on entropy characterization of audible acoustic signals

作　　者：谢达 鲁钟文 孙丽 杨磊 吴军 李国超[1] XIE Da;LU Zhongwen;SUN Li;YANG Lei;WU Jun;LI Guochao(School of Mechanical Engineering,Jiangsu University of Science and Technology,Zhenjiang 212003,CHN;Shaanxi Aircraft Industry Corporation Ltd.,Hanzhong 723200,CHN)

机构地区：[1]江苏科技大学机械工程学院,江苏镇江212003 [2]航空工业陕西飞机工业有限责任公司,陕西汉中723200

出　　处：《制造技术与机床》2025年第4期35-40,共6页Manufacturing Technology & Machine Tool

基　　金：国家青年科学基金项目(62203193)。

摘　　要：基于可听声信号的刀具磨损状态监测具备成本低、传感器安装便捷以及不干扰加工过程等优势,应用前景十分广阔。然而,可听声信号包含大量环境噪声,致使监测精度欠佳。为此,提出一种基于可听声信号熵特征的刀具磨损状态监测方法。首先,提取加工过程中可听声信号的7种熵特征;其次,采用相关性分析方法,选出皮尔逊相关系数(Pearson correlation coefficient,PCC)绝对值大于0.9的香农熵、小波熵、排列熵和近似熵特征;最后,采用长短时记忆(long short-term memory,LSTM)网络构建刀具磨损状态监测模型。试验结果显示,熵特征可抵御噪声干扰,且监测精度均方根误差(root mean square error,RMSE)为0.0041 mm。Tool wear monitoring based on audible signals is promising because of the advantages of low cost,easy installation of sensors,and no interference with the machining process.However,the audible signal contains a large amount of environmental noise,resulting in poor monitoring accuracy.To this end,a tool wear monitoring method based on the entropy characteristics of audible signals is proposed.Firstly,seven kinds of entropy features of audible signals in the machining process are extracted.Secondly,shannon entropy,wavelet entropy,permutation entropy and approximate entropy features with the absolute value of Pearson correlation coefficient(PCC)greater than 0.9 are preferred by correlation analysis.Finally,the tool wear monitoring model is constructed by using the long short-term memory(LSTM)network.The experimental results show that the entropy features can resist noise interference,and the root mean square error(RSME)of monitoring accuracy is 0.0041 mm.

关 键 词：熵特征 长短时记忆网络 刀具磨损状态监测 可听声信号 铣刀 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]