Fault detection on cutting tools based on wavelet neural network

被引：0
|
作者：
Xie, Ping [1 ]
Liu, Bin [1 ]
机构：
[1] Yanshan Univ., Qinhuangdao 066004, China
来源：
Jixie Gongcheng Xuebao/Chinese Journal of Mechanical Engineering | 2002年 / 38卷 / 02期
关键词：
Failure analysis - Learning systems - Neural networks - Nonlinear systems - Wavelet transforms;
D O I：
10.3901/jme.2002.02.108
中图分类号：
学科分类号：
摘要：
A fault detection method based on nonlinear model and learning system of wavelet network, which collects multi-source feature parameters of cutting tools, is proposed to realize the cutting tools on-line state detection. Then aiming at the problem of MIMO diagnosis system - the dimension disaster and the slow learning speed, the wavelet network is improved by optimization algorithm that can adjust and search for the wavelet parameter adaptively to find the optimum wavelet neurons. Finally, the simpler structure and quickly convergent velocity of the new algorithm is demonstrated by simulation results.
引用
收藏
页码：108 / 111
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