第一作者 孙惠斌 男，博士，副教授，1977年生

通信作者 牛伟龙 男，硕士，1989年生

基于希尔伯特黄变换的刀具磨损特征提取

孙惠斌，牛伟龙，王俊阳

(西北工业大学 机电学院，西安710072)

摘要：概述了希尔伯特黄变换(HHT)的基本理论和算法，对信号经过经验模态分解(EMD)后得到的固有模态函数(IMF)求取振幅均值，差值筛选出与刀具磨损相关的IMF分量，并对单分量固有模态函数求取边际谱，获取边际谱最大幅值点，建立他们与刀具磨损之间的映射关系，进行特征提取，将其作为神经网络的输入特征向量，结合希尔伯特三维时频谱进行刀具磨损状态的判断。研究结果证明，该方法可以作为刀具磨损监测中信号特征提取的一种简单和可靠的方法。

关键词：希尔伯特黄变换；小波去噪；固有模态函数；希尔伯特谱；边际谱

基金项目：陕西省自然科学基金(2013JM7001);西北工业大学基础研究基金( JC20110215);西北工业大学研究生创业种子基金

收稿日期：2013-11-01修改稿收到日期：2014-03-03

中图分类号:TH165；TN911文献标志码： A

Tool wear feature extraction based on Hilbert-Huang transformation

SUNHui-bin,NIUWei-long，WANGJun-yang(School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China)

Abstract:After presenting the basic theory and algorithm of Hilbert-Huang transformation (HHT), a tool signal was decomposed with the empirical mode decomposition (EMD) method and its intrinsic mode functions (IMFs) were gained to obtain their average amplitude. The IMF components related to tool wear were chosen using a difference screen. Meanwhile, the marginal spectrum of a single intrinsic mode function was obtained and its maximum amplitude point was then found. By establishing the mapping relationship between maximum amplitude points and tool wear, the features of tool wear were extracted. Regarding them as input eigen-vectors of a neural network, and combined with Hilbert spectra, the tool wear status judgment was implemented. The study results showed that this approach is a simple and reliable method to detect the level of tool wear.

Key words: Hilbert-Huang transformation; wavelet domain denoising; intrinsic mode function (IMF); Hilbert spectrum; marginal spectrum

刀具是加工过程中最常见也是最重要的加工要素之一，其磨损状态直接影响了加工工件的质量和精度。研究刀具磨损状态的监测具有重大的意义，不仅可以大大提高机械加工效率,而且可以降低加工成本，有着较大的经济效益。由于直接监测刀具磨损难度大，间接监测刀具磨损已成为一个重要研究方向。其主要技术路线是：

(1) 信号采集：通过力、加速度、声发射、功率等一些相应的传感器对刀具加工过程中的一些信号进行采集。……