| 基于小波神经网络的航空刀具磨损状态识别 |
| |
| 引用本文： | 聂鹏,谌鑫,徐涛,孙宝林.基于小波神经网络的航空刀具磨损状态识别[J].北京航空航天大学学报,2011,37(1):106-109. |
| |
| 作者姓名： | 聂鹏 谌鑫 徐涛 孙宝林 |
| |
| 作者单位： | 沈阳航空航天大学机电工程学院,沈阳,110136;沈阳航空航天大学机电工程学院,沈阳,110136;沈阳航空航天大学机电工程学院,沈阳,110136;沈阳航空航天大学机电工程学院,沈阳,110136 |
| |
| 基金项目： | 沈阳市人才引进专项基金资助项目(07SYRC04); 辽宁省教育厅重点实验室项目(LS2010117) |
| |
| 摘 要： | 针对航空零件的加工特点,采集刀具在不同磨损状态下的声发射(AE,Acoustic Emission)信号,对AE信号进行时频分析和小波变换,运用快速傅里叶变换(FFT, Fast Fourier Transform)以及db8小波5层分解,提取AE信号幅值的均方根和主能量频段的能量作为特征向量,对特征向量进行归一化处理后作为输入向量对小波神经网络进行训练.小波神经网络运用参数调整算法,在权值和阈值的修正中加入动量项.测试结果表明:AE信号对刀具磨损敏感的频率范围在10~150kHz,网络实际输出与期望结果的误差小于0.03,该方法能够对刀具不同磨损状态进行正确的识别.
|
| 关 键 词： | 航空加工 刀具磨损 小波神经网络 状态识别 |
| 收稿时间： | 2009-11-12 |
State recognition of tool wear based on wavelet neural network |
| |
| Nie Peng,Chen Xin,Xu Tao,Sun Baolin.State recognition of tool wear based on wavelet neural network[J].Journal of Beijing University of Aeronautics and Astronautics,2011,37(1):106-109. |
| |
| Authors: | Nie Peng Chen Xin Xu Tao Sun Baolin |
| |
| Institution: | Mechanical and Electrical Engineering Institute, Shenyang Aerospace University, Shenyang 110136, China |
| |
| Abstract: | In connection with the processing characteristics of aviation parts,acoustic emission(AE) signals of tool which in different wear state were acquired.Time-frequency analysis and wavelet transform were utilized on the AE signals.Fast fourier transform and db8 wavelet decomposition were used to extract the amplitude root-mean-square value and the main band energy value which were considered as eigenvectors of AE signals.Then the eigenvectors were normalized and taken as input vector for the training of wavele... |
| |
| Keywords: | aviation machining tool wear wavelet neural network condition identification |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《北京航空航天大学学报》浏览原始摘要信息 |
| 点击此处可从《北京航空航天大学学报》下载免费的PDF全文 |
|
