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将一种基于小波分析和神经网络的多传感器融合技术应用于刀具磨损监测系统。介绍了小波分析和神经网络的理论基础;给出了刀具磨损在线监测系统的组成和基于小波分析和神经网络的多传感器融合技术在刀具磨损在线监测系统应用过程。多种传感器采集的信号通过小波分析提取其特征值,将特征值作为神经网络的输入,对比识别刀具磨损状态。经实验验证,基于小波分析和神经网络的多传感器融合技术能有效识别刀具磨损状态。
A multi-sensor fusion technology based on wavelet analysis and neural network is applied to the tool wear monitoring system. The theoretical basis of wavelet analysis and neural network is introduced. The composition of on-line monitoring system of tool wear and the application of multi-sensor fusion technology based on wavelet analysis and neural network in the on-line monitoring system of tool wear are given. A variety of sensors to extract the signal through wavelet analysis to extract its eigenvalues, the eigenvalues as the input of the neural network, compared to identify tool wear status. The experimental verification shows that the multi-sensor fusion technology based on wavelet analysis and neural network can effectively identify the tool wear state.