基于奇异值分解的随机共振特征提取研究

针对强背景噪声下信噪比极低的微弱特征信号的识别问题,提出了基于奇异值分解的随机共振特征提取方法.该方法首先利用奇异值分解对实际采样信号进行预处理和重构,然后寻找到特征信号分量与噪声强度相匹配的分量信号.此分量信号再经过非线性双稳系统的随机共振处理,可实现从强噪声背景中检测极微弱的特征信号.     

调制与解调用于随机共振的微弱周期信号检测

提出了调制随机共振方法，实现了在大参数条件下从强噪声中检测微弱周期信号.将混于噪声中的较高频率的弱信号经调制变为一差频的低频信号作用于随机共振体系，该低频信号满足绝热近似理论，因而能产生随机共振；再经解调可获得埋于噪声中的原较高频率的弱信号.对埋于噪声中的未知频率，可采用连续改变调制振荡器的频率，以获得一个适当的差频信号输入到随机共振体系，根据输出信号共振谱峰的变化经解调而得待检弱信号的未知频率.该方法应具有较高的应用前景. 关键词： 调制与解调 非线性双稳系统 随机共振 微弱信号检测     

阵列调制随机共振在微弱信号特征提取方面的应用

论述了阵列调制随机共振方法在强噪声背景下多频微弱信号特征提取中的工作原理和实现步骤;采用预先设定系统参数的多个并联非耦合随机共振单元形成阵列,将被测强噪声背景下的多频微弱信号分别与不同频率的载波进行调制,生成多个差频均为0.01Hz的信号作为各对应随机共振单元的激励信号,采用龙格-库塔算法求取各单元输出并进行频谱分析,根据0.01Hz处的信噪比判断在微弱信号中是否存在载波频率与差频值之和大小的频率分量,最后综合各个随机共振单元的检测结果生成微弱信号的频率特征向量;仿真结果表明,阵列调制随机共振在微弱信号特征提取方面效果明显,具有很好的应用前景。     

α稳定噪声环境下多频微弱信号检测的参数诱导随机共振现象

本文将α稳定噪声与双稳随机共振系统相结合, 研究了不同α稳定噪声环境下高低频(均为多频)微弱信号检测的参数诱导随机共振现象, 探究了α稳定噪声的特征指数α(0 < α ≤ 2)和对称参数β (-1≤ β ≤ 1)及随机共振系统参数a, b对共振输出效应的作用规律. 研究结果表明, 在不同分布的α稳定噪声环境下, 通过调节系统参数a和b均可诱导随机共振来实现多个高、低频微弱信号的检测, 且存在多个a, b参数区间均可诱导随机共振, 这些区间不随α或β的变化而变化; 在高、低频微弱信号检测中, α或β对随机共振输出效应的作用规律相同. 本研究结果将有助于α稳定噪声环境下参数诱导随机共振现象中系统参数的合理选取, 进而可为实现基于随机共振的多频微弱信号检测方法的工程应用奠定基础. 关键词： 随机共振 α稳定噪声')" href="#">α稳定噪声 多频微弱信号检测 平均信噪比增益     

滤波器与随机共振结合检测微弱信号

基于双稳随机共振系统及滤波器的不同特性,本文提出了一种将两者结合起来检测微弱周期信号的方法,先用自适应前置滤波器对输入的弱周期信号及噪声进行滤波,再使其通过双稳随机共振系统,进而检测出弱信号。对比只有双稳随机共振的系统,仿真结果表明此时的输出信号中待测信号频谱幅度得到了很大的提高,且周围的干扰信号也得到了明显的削弱,即两者的结合使用可以更好的检测出微弱信号,这对强噪声背景下的信号检测有很强的实用性。     

二次采样用于随机共振从强噪声中提取弱信号的数值研究

提出了二次采样的随机共振(SR)技术，并利用该技术，实现了绝热近似理论在大参数条件下，从强噪声中提取弱信号的目标.为便于应用，研究了二次采样随机共振方法在弱信号检测中相关参数之间的关联性.数值分析表明，该法在信号分析方面有着潜在的应用价值，有望将来应用于实测信号的数据处理. 关键词： 随机共振 弱信号检测 数值分析 非线性系统     

基于混沌和随机共振的微弱信号检测

推导了分数阶线性振子系统响应的一阶稳态矩的频率不变性和相移特性, 并通过理论分析得出, 在随机共振机制下, 分数阶线性振子对系统响应一阶稳态矩的幅值具有放大作用. 构造Duffing混沌振子检测器, 利用混沌系统对参数摄动的敏感性以及对噪声的免疫能力实现弱信号检测. 数值模拟证实, 该方法可以有效地从噪声背景中将微弱正弦信号检测出来, 并且相对传统的混沌检测方法能显著降低信噪比检测门限.     

乘性和加性α稳定噪声环境下的过阻尼单稳随机共振现象

本文将α稳定噪声与单稳随机共振系统相结合,研究了乘性和加性α稳定噪声环境下的过阻尼单稳随机共振现象,探究了α稳定噪声特征指数α(0α2)、对称参数β(-1β1),单稳系统参数a及乘性α稳定噪声放大系数D对共振输出效应的作用规律.研究结果表明,在不同分布的α稳定噪声环境下,在一定范围内通过调节a或D均可诱导随机共振来实现单个或多个高、低频微弱信号的检测,且a和D分别存在一个最优值可使系统产生最佳的随机共振效应;不同α或β均可对系统共振输出效应产生规律性的影响,且α或β在高、低频微弱信号检测中的作用规律相同;在研究α稳定噪声环境下单、多频单稳随机共振现象时所得结论是相同的.本研究结果可为实现α稳定噪声环境下单稳随机共振系统参数的自适应调节奠定基础.     

一种改进的微弱NQR信号检测算法

核四极矩共振（NQR）是一种固态射频谱分析技术,可用于检测高危险爆炸物. 然而NQR信号本身非常弱,并且易受线圈的热噪声和外部射频干扰的影响,低信噪比限制了NQR的实际应用. 该文提出一种改进的微弱NQR信号检测算法. 首先利用Hankel矩阵方式下奇异值分解的方法,有效地抑制射频干扰和噪声,并将NQR信号分离出来. 然后提出了一种基于MUSIC谱估计的非线性最小二乘检测器,它既保证了高的频率分辨率,又大大降低了运算量. 仿真数据和实测数据结果表明该算法的有效性.     

基于小波分解和Duffing振子的变尺度微弱信号检测

传统的时频分析方法在对周期性微弱信号进行检测时,提取的信息具有信噪比不高的缺点,从而影响了检测效果,为此,利用Duffing振子混沌系统对噪声的强免疫力的特征,提出了一种基于小波分解和混沌阵子的混合微弱信号检测方法;首先,采用小波变换对信号进行分解,通过小波变换的平滑作用实现对含噪微弱信号的离散处理,并设计了一种根据阈值来确定分解层数的方法,然后将降噪后的重构信号作为Duffing阵子的周期驱动力并入混沌系统,采用混沌Duffing阵子阵列实现在强噪声背景下的微弱信号检测,并提出了一种临界状态策动力幅值和初始相位的自适应确定方法;在Matlab7仿真环境下进行实验,结果表明:文中方法能有效地对湮没在强噪声下的微弱信息进行检测,具有信号检测信噪比高,重构信号频率较其它方法更接近于真实频率,具有较强的可行性。     

A weak signal detection method based on adaptive parameter-induced tri-stable stochastic resonance

Aiming at detecting the weak signal in a strong noise background, an enhanced weak signal detection method based on adaptive parameter-induced tri-stable stochastic resonance is proposed. Firstly, because the system can switch among the monostable, bistable and tri-stable state, the potential function characteristic of tri-stable systems is studied by analyzing the potential function curves with different system parameters. And the dynamic characteristics of system parameters on the depth of the potential well is analyzed. The ranges of R and the system parameters are determined, which is essential for ensuring the system is tri-stable state. Secondly, the range of R is used as the constraint condition and the average output signal-to-noise ratio is used as the fitness function of the adaptive algorithm. The system parameters a, b, c are optimized by the differential evolution particle swarm optimization (DEPSO) method to obtain the best output effect. Finally, the proposed adaptive parameter-induced tri-stable stochastic resonance method is adopted to detect the mixed multiple high-frequency weak signal. The detection results are compared with that of adaptive bistable stochastic resonance. At the meanwhile, the method is also applied to detect the fault signal of single crystal furnace. Both the simulation analysis and experiment results show that the proposed method can effectively improve the output signal-to-noise ratio and detect multi-frequency weak signal in the strong noise background.     

Study on the mean first-passage time and stochastic resonance of a multi-stable system with colored correlated noises

The mean first-passage time (MFPT) and the weak signal detection method of stochastic resonance (SR) on multi-stable nonlinear system under color correlated noise are studied. Using the uniform color noise approximation method, the Fokker-Planck equation of the system is obtained, and the steady-state probability density function of the multi-stable system driven by the multiplicative noise and additive noise is derived. On the basis of this, the formula of MFPT is derived, and the influence of parameters on the MFPT is analyzed. The problem of weak signal detection under color noise background is studied based on multi-stable SR. The results of simulation and experiment show that the method can effectively extract the frequency feature of weak signal in the background of color noise.     

Detection of multi-frequency weak signals with adaptive stochastic resonance system

Aiming at the poor detection rate of multi-frequency weak signals under a strong background of noise, a novel method based on adaptive stochastic resonance (SR) theory is proposed in this paper. The optimal parameters can be obtained automatically via measurement by establishing an adaptive SR system model and using the reverse location method. After passing through the adaptive SR system, the spectrum values of all eight signals greatly improve, the largest spectrum value gain increases from 12.41 to 2033 when the frequency is 0.01?Hz, which is an improvement of a factor of 162.8, and the signal-to-noise ratio (SNR) gain of the whole system is 10.3134?dB. Under the condition of different input noise intensities and signal amplitudes, the mean SNR of the system increases from –13.1136 to –2.7614?dB, which is a 78.9% increase, and the largest SNR gain is 13.4702?dB when the noise intensity D?=?1.2 and signal amplitude A?=?0.11. Compared to the single optimal spectrum value, when defining multiple optimum spectrum values as the SNR criterion, the detection sensitivity is less than 0.35 when the input noise intensity is between 0.5 and 2.5, and the sensitivity value is 6.29 times higher when D?=?2.5. The system successfully realizes the adaptive detection of twelve weak signals, and the SNR gain is 7.9743?dB, which improves the channel capacity of signal detection. The experimental results demonstrate the high efficiency and strong applicability of the system, improving the signal processing efficiency and speed of signal transmission.     

α稳定噪声环境下过阻尼系统中的参数诱导随机共振现象

本文采用随机模拟方法, 研究了过阻尼振子系统在α稳定噪声环境下的参数诱导随机共振现象. 结果表明, 在α噪声环境下, 调节系统参数能够诱导随机共振现象; 而且调节非线性项参数时, 随机共振效果随α稳定噪声的指数的减小而减弱, 但当调节线性项参数时, 随机共振效果则随着α稳定噪声的特征指数的减小而增强. 本文的结论在α稳定噪声环境下, 利用参数诱导随机共振原理进行弱信号检测方面具有重要的理论意义, 并有助于理解不同α稳定噪声对一般随机共振系统的共振效果的影响.     

Asymmetric delay feedback stochastic resonance detection method based on prior knowledge particle swarm optimization

For the adjustable parameters stochastic resonance system, the selection of the structural parameters plays a decisive role in the performance of the detection method. The vibration signal of rotating machinery is non-linear and unstable, and its weak fault characteristics are easily concealed by noise. Under strong background noise interference, the detection of fault features is particularly challenging. Therefore, a type of weak fault feature extraction method, named knowledge-based particle swarm optimization algorithm for asymptotic delayed feedback stochastic resonance (abbreviated as KPSO-ADFSR) is proposed. Through deduction under adiabatic approximation, we observe that both the asymmetric parameters, the length of delay and the feedback strength, impact the potential function. After adjusting the asymmetric parameters of the system, the output signal-to-noise ratio (SNR) is used as the fitness function, and the setting of the relationship between the noise intensity and barrier height is used as the prior knowledge of the particle swarm algorithm. Through this algorithm, the delay length and the feedback strength are optimized. This method achieves global optimization of system parameters in a short time; it overcomes the shortcomings of the traditional stochastic resonance method, which has a long convergence time and tends to easily fall into local optimization. It can effectively improve the detection of weak fault features. In the bearing rolling body pitting corrosion failure experiment and steel field engineering experiment, the proposed method could extract the characteristics of a weak fault more effectively than the traditional stochastic resonance method based on the standard particle swarm algorithm.     

基于随机共振原理的分段线性模型的理论分析与实验研究

提出了一种分段线性双稳态模型, 推导了模型的解析关系及其输出信噪比, 通过对该模型与连续双稳态模型的对比分析和仿真实验, 证明了该模型的优越性.该模型具有参数之间相互独立、易于调节的特点. 在对模型分析与数值仿真的基础上, 通过电路对强噪声背景下的微弱周期信号检测进行了实验研究. 结果表明分段线性随机共振模型能够有效实现对微弱周期信号的检测, 并能显著增强输出信噪比.     

Analysis of weak signal detection based on tri-stable system under Levy noise

Stochastic resonance system is an effective method to extract weak signal.However,system output is directly influenced by system parameters.Aiming at this,the Levy noise is combined with a tri-stable stochastic resonance system.The average signal-to-noise ratio gain is regarded as an index to measure the stochastic resonance phenomenon.The characteristics of tri-stable stochastic resonance under Levy noise is analyzed in depth.First,the method of generating Levy noise,the effect of tri-stable system parameters on the potential function and corresponding potential force are presented in detail.Then,the effects of tri-stable system parameters w,a,b,and Levy noise intensity amplification factor D on the resonant output can be explored with different Levy noises.Finally,the tri-stable stochastic resonance system is applied to the bearing fault detection.Simulation results show that the stochastic resonance phenomenon can be induced by tuning the system parameters w,a,and b under different distributions of Levy noise,then the weak signal can be detected.The parameter intervals which can induce stochastic resonances are approximately equal.Moreover,by adjusting the intensity amplification factor D of Levy noise,the stochastic resonances can happen similarly.In bearing fault detection,the detection effect of the tri-stable stochastic resonance system is superior to the bistable stochastic resonance system.