基于Lorenz系统的微弱谐和信号检测

基于非共振参数激励混沌抑制原理,利用受控Lorenz系统实现强噪声背景下微弱谐和信号的检测。根据检测系统经平均法和重整化方法处理后的参数等效关系,确定使系统动力学行为由周期轨道突变为稳定平衡点的检测参数临界值。仿真结果表明此系统可以准确检测出强噪声背景下的微弱谐和信号。相比于现有的混沌振子检测方法,此方案可由理论分析得到参数阈值的准确范围,且判决准则简单,有利于实现自动检测。     

基于Duffing方程的齿轮故障检测方法研究

提出一种基于修正Duffing方程间歇混沌理论的弱信号检测新方法.在该检测方法中,当输入信号频率与系统激励频率之间存在微小偏差时系统输出为间歇混沌信号,且其频率偏差可由输出混沌信号的统计特性进行估计.数值仿真结果表明这种方法可以准确检测出信噪比很低的微弱正弦信号.最后,利用实验平台采集齿轮振动声信号数据,分别采用频谱分析法和混沌弱信号检测法对实验数据进行检测,结果表明混沌弱信号检测法具有更高的检测精度和更强的抗干扰能力.     

基于混沌理论的微弱信号检测及自跟踪扫频电路实现

为解决工程实际中因待测信号常常被淹没在噪声背景中而传统信号检测方法难以检测等问题,将基于混沌理论的非线性信号检测技术应用到实际工程故障诊断中,开展了基于Duffing振子的微弱信号检测原理的分析,建立了混沌振子与微弱信号检测之间的关系,提出了基于Duffing振子的微弱信号检测方法,利用混沌系统相变对周期小信号的敏感性和对噪声具有免疫力的特点,设计制作了基于Duffing振子的微弱信号检测电路;对微弱信号检测的自适应进行了研究,利用AVR单片机及AD9850等芯片实现了信号检测电路的自动跟踪扫频功能,最后开展了该信号检测电路对不同频率微弱信号的检测试验。研究结果表明,用该电路可以实现在工程中常见的噪声背景下的中、低频率微弱周期信号的检测。     

基于混沌-现代谱估计正弦信号检测方法

介绍了测量正弦信号的频域方法--传统谱估计方法及现代谱估计方法.在此基础上提出了将现代谱估计方法与著名混沌系统(杜芬振子系统)构成混合检测系统,共同检测微弱正弦信号方案.给出仿真系统框图及仿真结果.结果表明用此方法在估计正弦信号时,幅度测量精度被提高,此方法是进一步发展弱信号检测的有效途径之一.     

微弱正弦信号的全频域检测方法

基于变型蔡氏电路模型的锁频功能,将被检测信号作为变容二极管的调制信号的一部分,实现了对微弱正弦信号的检测.用四阶龙格-库塔方法对系统相变与调制信号的关系进行了数值实验,发现调制信号频率与系统相变之间存在周期关系,利用这种周期关系,可以实现全频域信号检测.     

基于混沌理论的滚动轴承故障信号检测

针对滚动轴承早期故障信号十分微弱的问题,提出采用Duffing混沌振子对故障微弱信号进行检测的方法。对Duffing方程进行改进,实现对任意频率微弱信号的检测。分析微弱周期信号相位角对检测系统的影响,提出采用多相位混沌振子阵列来消除微弱周期信号相位角对检测系统的影响。通过仿真实验,确定检测系统由3个混沌振子构成。使用该检测系统成功检测出轴承外圈故障微弱信号,相比传统的混沌振子检测系统,缩小了检测盲区,提高了检测信噪比。     

微弱正弦信号的一种新的混沌检测系统

用特定的Duffing-Holmes方程实现了强噪声背景下弱正弦信号的有效检测.并用梅尔尼科夫函数给出系统出现混沌状态的阈值.仿真实验表明该混沌系统对微弱正弦信号非常敏感,对任意零均值噪声均具有极强的抑制能力,系统信噪比工作门限可达到-90dB.     

基于Duffing振子的噪声背景下微弱周期信号检测

为有效地实现噪声背景下弱信号的提取,阐述了间歇混沌模型Duffing振子的混沌特性。利用Duffing振子对微弱信号具有敏感性、对噪声与频率差较大的周期干扰信号具有免疫力的特性,研究了基于Duffing振子在噪声条件下检测微弱周期信号、复合频率信号和未知频率信号的方法,用数值仿真验证了该方法的可行性。研究结果表明,基于Duffing振子的信号检测方法对极微弱周期信号检测有其独到的优势,其频率误差率在控制范围之内。     

用相位差值判别Duffing振子相变的新方法

针对Duffing振子微弱信号检测中相变(即混沌态与大尺度周期态状态的转变)判别算法复杂、计算量大等问题,推导了对Holmes型Duffing振子的增量谐波法求解方程,根据计算结果分析了相变过程中系统解的谐波特性,利用系统解与周期策动力的相位变化规律,提出一种用相位差值来判别相变的新方法.数值仿真和实验测试表明,在强噪声背景下系统相轨迹波动严重,但该方法仍能对系统相变实时准确地判别.     

微弱正弦信号的互相关-混沌系统合成检测技术

将常规互相关检测方法与混沌检测方法相结合,发挥各自的优势构成一个新的微弱正弦信号检测系统.理论分析及仿真实验表明该检测系统对被强噪声覆盖的微弱正弦信号非常敏感,对任何零均值噪声具有极强的抑制能力.     

Research and Simulation on Weak Signal Detection Based on Duffing Oscillator and Damping Ratio Perturbation

The chaotic system is sensitive to the initial value,and this property can be applied in the weak signal detection.There are periodic,critical and chaotic states in a chaotic system.When the system is in the critical state,a small perturbation of system parameter may lead to a qualitative change of the system’s state.This paper introduces a new method to detect weak signals by the way of disturbing the damping ratio.The authors choose the duffing equation,using MATLAB to carry on the simulation,to study the changes of the system when the signal to be measured is added to the damping ratio.By means of observing the phase locus chart and time domain chart,the weak signal will be detected.     

基于Duffing振子检测频率未知微弱信号的新方法

针对现有混沌振子难以检测频率未知微弱信号这一难点,提出利用Duffing振子输出值的方差峰值结合遗传算法检测淹没在强噪声背景中频率未知微弱信号的一种新方法。从分析混沌系统结构参数的阈值入手,讨论了周期策动力的频率、初始相位和噪声对系统运行状态的影响;研究系统输出值方差与系统状态的对应关系,探讨待测信号频率以及与周期策动力之间相位差对状态变量方差和状态转换时间的影响。由此,提出采用具有相位偏移的Duffing振子阵列覆盖全相位,并结合遗传算法,优化求解不同频率输入信号下系统输出值方差的极值,以此得到待测信号频率的方法。该方法解决了现有混沌振子类检测方法必须已知信号频率的限制。实验结果证明了本方法能准确、快速地检测待测信号频率。新方法的状态判定简便、检测精度高、更为灵活、适应性强,为微弱信号的检测提供了新的手段。     

Method of Weak Signals Detection Based on Chaos Suppression in the Nonresonant Parametric Drive

The evolution of chaotic state of Lorenz system on the familiar parameter space orbit is analyzed.Based on the principle of chaos suppression with nonresonant parametric drive,the model of detecting weak periodic signals in strong noise is built.According to the parametric equivalent relationship obtained using averaging method and renormalization method,the critical values of detection parameters are determined,which lead to a sudden change of system dynamical behavior from periodic orbit to stable equilibrium point.Simulation results show that weak periodic signals in strong noise can be detected accurately with the proposed system.The method can obtain accurate range of parameter threshold through theoretical analysis,and the detection criterion is rather simple,which is more convenient for automatic detection.     

基于变参数随机共振的齿轮早期故障诊断

文章利用随机共振理论检测齿轮早期的微弱信号,以信号分段关联维数作为分形诊断分类原理的特征量,对齿轮未知信号状态进行判断,取得了较好的效果。     

Lyapunov指数算法在应答器信号混沌振子检测中的应用

针对为提高在强噪声环境下应答器上行链路传输信号的检测精度,利用混沌系统对初始条件敏感以及对噪声免疫的特性,将混沌振子应用到应答器上行链路信号检测解调中.结合微弱信号Duffing振子检测原理和应答器上行链路信号特征,给出了使用Duffing振子检测应答器信号的方法和步骤,并使用Lyapunov指数算法计算Duffing振子检测系统的临界阈值,定量判断系统的输出状态,实现应答器信号的解调.在理论分析的基础上,进行了实验仿真验证.仿真结果表明,基于Lyapunov指数算法的应答器信号混沌振子检测方法提高了阈值设置的准确性和效率,并确保了应答器信号检测的可靠性.     

Adaptive stochastic resonance method for weak signal detection based on particle swarm optimization

In order to solve the parameter adjustment problems of adaptive stochastic resonance system in the areas of weak signal detection,this article presents a new method to enhance the detection efficiency and availability in the system of two-dimensional Duffing based on particle swarm optimization.First,the influence of different parameters on the detection performance is analyzed respectively.The correlation between parameter adjustment and stochastic resonance effect is also discussed and converted to the problem of multi-parameter optimization.Second,the experiments including typical system and sea clutter data are conducted to verify the effect of the proposed method.Results show that the proposed method is highly effective to detect weak signal from chaotic background,and enhance the output SNR greatly.     

基于Lyapunov指数的超声导波检测技术

为了提高超声导波的检测灵敏度,提出了一种基于杜芬方程Lyapunov指数特性的超声导波识别方法,该方法利用了杜芬方程对系统参数的敏感性及其对噪声信号的免疫特性。首先,分析了杜芬方程检测导波信号的数学原理;其次,讨论了如何设定检测系统参数,给出了可用于检测导波信号的杜芬系统;最后,通过分析比较噪声和导波信号对Lyapunov指数的不同影响,证明了该方法识别强噪声下弱超声导波的有效性。数值算例表明,通过合理设置杜芬方程参数使系统处于混沌状态,当输入导波信号和混有噪声的导波信号时,系统由混沌状态转变为极限环运动,利用杜芬系统状态改变可实现对强噪声下弱超声导波的识别,该方法可有效延长超声导波的检测范围和提高检测小缺陷的灵敏度。     

A joint adaptive wavelet filter and morphological signal processing method for weak mechanical impulse extraction

Periodical impulses are vital indicators of rotating machinery faults. Therefore, the extraction of weak periodical impulses from vibration signals is of great importance for incipient fault detection. However, measured signals are often severely tainted by various noises, which makes the detection of impulses rather difficult. As such, a proper signal processing technique is necessary. In this paper, a hybrid method comprised of wavelet filter and morphological signal processing (MSP) is proposed for this task. The wavelet filter is used to eliminate the noise and enhance the impulsive features. Then, the filtered signal is processed by the morphological closing operator and a local maximum algorithm to isolate periodical impulses. To select the proper parameters of the joint approach, i.e., the center frequency, the bandwidth of wavelet filter, and the length of flat structuring elements (SE), a novel optimization algorithm based on differential evolution (DE) is developed. The results of simulated experiments and bearing vibration signal analysis verify the effectiveness of the proposed method.     

Applications of chaotic oscillator in machinery fault diagnosis

The forward detecting method is used to detect weak periodic signals 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. Based on the method above, in this paper, a similar method, which is devised in a reverse way, is presented. The method detects the change of a weak signal by identifying the transformation of the chaotic oscillator from the large-scale periodic state to the chaotic state when a weak external signal is applied. This paper discusses and summarizes the features and scopes of both methods in their application in the field of machinery fault diagnosis. Satisfactory results have been achieved when using both of them in the fault diagnosis of rolling bearings and automobile gearboxes. The paper also presents how to use symbol sequence statistics to automatically identify the state transformation of the chaotic oscillator.