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《Machine Tool & Hydraulics》 2007-02
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On-line Tool Condition Monitoring Based on Recursive Wavelet Neural Networks

ZHU Yunfang1, DAI Chaohua2, CHEN Weirong2 (1.Southwest Jiaotong University Emei Branch, Emei Sichuan 614202,China; 2.School of Electric Eng, Chengdu Sichuan 610031,China)  
Based on the Super-Gaussian function, a general method of recursive mother wavelet was introduced, and an optimal method of wavelet construction was proposed.The time-frequency characteristics of recursive wavelet were analyzed.A close-type wavelet network was constructed using the theory of frame wavelet network and the intermediate value theorem of continuous functions.The AE signals of tool conditions were decomposed using a recursive wavelet from which the features were extracted and delivered to the wavelet network for fault recognition, and the recognition rate is up to 100%.
【CateGory Index】: TP183;TP274.4
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