摘要
针对数控机床主轴电流分析中微弱电流信号被噪声淹没的问题,提出了一种将调制随机共振和D-J阈值噪声估计技术用于电流弱信号的特征提取方法。利用调制随机共振技术获得了微弱电流信号的特征频率,在频域上采用D-J阈值收缩方法对所测得的电流信号进行噪声估计,从中分离出有用信号,并根据随机共振原理对该有用信号进行了幅值估计。仿真和实验结果表明,该方法可以对数控机床主轴电流信号中的微弱电流特征信号进行频率识别和幅值估计,克服了传统电机电流分析方法无法识别微弱电流信号的缺点,能够准确地提取出微弱电流信号中的特征频率,可对微弱特征信号进行比较准确的幅值估计,因此具有较强的工程实用价值。
To extract weak current signal features in the spindle current of machine tools, which are often buried in noises, a signal processing method with modulated stochastic resonance and D-J threshold theory is proposed. Modulated stochastic resonance is adopted to amplify the features of weak current signals, and then D-J threshold theory is chosen to make noise estimation for extracting the useful signals. The amplitude is estimated by the stochastic resonance. The simulated and experimental results show that this method enables to accurately identify the weak feature frequencies and estimate the amplitudes of weak current signals properly.
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2013年第9期83-87,共5页
Journal of Xi'an Jiaotong University
基金
国家科技重大专项基金资助项目(2011ZX04016-101)
作者简介
陈晓光（1988-），男，博士生；
徐光华（通信作者），男，教授，博士生导师。
