| 基于小波神经网络监测刀具状态的研究 |
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| 引用本文： | 冯冀宁,刘彬,任玉艳.基于小波神经网络监测刀具状态的研究[J].自动化与仪表,2003,18(3):8-10. |
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| 作者姓名： | 冯冀宁 刘彬 任玉艳 |
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| 作者单位： | 燕山大学,电气工程学院,河北,秦皇岛,066004 |
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| 摘 要： | 针对切削过程中振动信号和AE信号的特点，提出一种基于小波分析和BP神经网络的刀具磨损监测系统。该系统能融合振动和AE信号的特征，描述信号特征与刀具状态的非线性关系，以此识别刀具状态。试验表明基于小波神经网络的刀具磨损状态监剩系统是有效的。
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| 关 键 词： | 神经网络 小波分析 刀具 状态监测 振动 AE信号 磨损 |
| 文章编号： | 1001-9944(2003)03-0008-03 |
Study on Tool Condition Monitoring Using Wavelet Neural Network |
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| FENG Jin ning,LIU Bin,REN Yu yan.Study on Tool Condition Monitoring Using Wavelet Neural Network[J].Automation and Instrumentation,2003,18(3):8-10. |
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| Authors: | FENG Jin ning LIU Bin REN Yu yan |
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| Abstract: | Aim for the features of vibration signal and AE signal in cutting proceeding ,a tool condition monitoring systerm based on wavelet and BP neural network is gived ,the signal are fused and the nonlinear relationship between signal feature and tool condition is described in order to identify tool condition using the system. The experimental results show it is very effective to monitor tool condition. |
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| Keywords: | BP nerural network wavelet tool monitoring vibration signal AE signal |
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