Chaotic Oscillator Detection Based on Empirical Mode Decomposition and Its Application

Fengli Wang, Deyou Zhao
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引用次数: 3

Abstract

In order to accurately make out early fault diagnosis of rotor system with weak signal, a novel fault diagnosis method based on empirical mode decomposition(EMD) associated with chaos oscillator was presented. Weak periodic signals can be detected by identifying the transformation of the chaotic oscillator from the chaotic state to the large-scale periodic state when a weak external periodic signal is applied. As the actual signal is generally a mixture of signal and noise, and interested weak signal is usually submerged in strong background and noise signals. EMD is proposed to remove component interference, and actual signal is divided into finite intrinsic mode functions (IMFs), so that weak characteristic signal can be separated from background and noise signals, and a reliable and convenient measure for weak periodic signal detection by Duffing oscillator can be completed efficiently. Numerical simulation and experimental results show the validity of the presented method.
基于经验模态分解的混沌振荡器检测及其应用
为了对转子系统微弱信号进行准确的早期故障诊断,提出了一种基于混沌振子的经验模态分解(EMD)故障诊断方法。通过识别外部微弱周期信号作用下混沌振荡器由混沌状态向大尺度周期状态的转变,可以检测出弱周期信号。由于实际信号通常是信号和噪声的混合,感兴趣的弱信号通常被淹没在强背景和噪声信号中。提出了一种消除分量干扰的EMD方法,将实际信号分解为有限个本征模态函数(IMFs),将微弱特征信号与背景和噪声信号分离,有效地完成了Duffing振荡器微弱周期信号检测的一种可靠、便捷的方法。数值仿真和实验结果表明了该方法的有效性。
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