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《Journal of Vibration and Shock》 2015-04
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Tool wear feature extraction based on Hilbert-Huang transformation

SUN Hui-bin;NIU Wei-long;WANG Jun-yang;School of Mechanical Engineering,Northwestern Polytechnical University;  
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.
【Fund】: 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
【CateGory Index】: TG71;TH117.1
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