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。
Abstract
After presenting the basic theory and algorithm of Hilbert-Huang Transform (HHT), the signal is decomposed through the empirical mode decomposition (EMD) method to get the intrinsic mode function (IMF) in order to obtain the average amplitude. The IMF component which, related with tool, is chosen through the difference of screening. Meanwhile, the marginal spectrum of single intrinsic mode function is obtained and its maximum amplitude is then found. By establishing the mapping relationship with tool wear, the feature extraction is achieved. Regarding them as the input vector of Neural Network, and combined with the Hilbert spectra, the tool wear judgment is being processed. The studies shows that this approach can be a simple and reliable method for detecting the level of tool wear.
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
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Key words
Hilbert-Huang transform /
wavelet domain denoising /
intrinsic mode function /
Hilbert spectrum /
marginal spectrum
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孙惠斌;牛伟龙;王俊阳.
基于希尔伯特黄变换的刀具磨损特征提取[J]. 振动与冲击, 2015, 34(4): 158-164
Sun Hui-bin;Niu Wei-long;WANG Jun-yang.
Tool Wear Feature Extraction Based on Hilbert-Huang Transform[J]. Journal of Vibration and Shock, 2015, 34(4): 158-164
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脚注
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