基于单势阱随机共振的多频周期微弱信号检测

经典随机共振（SR）应用于微弱信号检测,在取得较好效果的同时也带来了最佳系统参数调节困难的问题.单势阱随机共振（SSR）只需调整一个系统参数,更容易调整系统到最佳状态.在研究了单势阱随机共振理论基础上,对多频周期信号加上与实际更为接近的色噪声,采用互相关系数作为测度指标,运用自适应方法寻找系统最优参数后,系统设置该参数,从待测信号中检测出多频周期微弱信号.实验结果表明单势阱随机共振对多频周期微弱信号具有较好的检测效果.     

α噪声下自适应非线性耦合双稳随机共振弱信号检测

在α噪声条件下,为实现微弱周期信号的检测,提出了以平均信噪比增益(A-SNRI)为衡量指标的自适应非线性耦合双稳随机共振系统。先通过调节α噪声参数,将此α噪声与待测信号输入非线性耦合双稳系统中,再通过自适应算法,得出最优系统参数以及耦合系数。实验结果表明,周期信号通过自适应非线性耦合双稳系统后发生了随机共振现象,其频谱图出现了陡峭的尖峰且信噪比增益效果提升明显,说明自适应非线性耦合双稳随机共振系统能够有效地检测出淹没在α噪声中的信号。     

采用自适应随机共振技术进行微弱局部放电信号检测

为实现对微弱放电目标的定位,提出了一种基于自适应随机共振技术检测微弱局部放电信号的方法。分别以具有不同持续时间的双指数函数和双指数衰减振荡函数模拟局部放电信号,加入强高斯噪声;利用该方法提取微弱放电信号,并通过实验验证了该方法的准确性。结果表明,在不同强度的噪声环境中,当尺度变换系数与信号采集频率的数量级相当、系统主要参数选取为信号持续时间的数十倍时,系统可达到随机共振状态,噪声部分能量将转移至放电信号,使得微弱的放电信号得到增强。该方法检测效果优于匹配滤波,为微弱局部放电信号检测提供了一种新方法。     

基于自适应随机共振的异步电动机转子断条故障检测

异步电动机转子断条故障检测属于强噪声背景下故障的微弱信号检测问题。由于不同转差率下电机具有不同的转子断条故障特征频率，给随机共振最佳系统参数调节增加了难度。针对上述问题，在已有Hilbert模量法的基础上，提出了基于自适应随机共振的异步电动机转子断条故障检测方法。通过应用小波消噪、随机共振技术，改进了转子断条故障特征信号的检测灵敏度。随机共振参数的自适应调节进一步增强了该方法的实用性，与传统方法相比，该方法在各种情况下尤其是在噪声强度、电机类型及运行状况未知时，仍能有效检测转子断条故障，具有较好的自适应性。数值仿真和实验分析证明了该方法的有效性。     

频谱监测中的多频随机共振检测

基于频段分离思想设计能够完成频谱监测中多频微弱信号的双稳态随机共振检测方案。使用归一化尺度变换对高频段范围内周期信号进行随机共振检测仿真实验;针对随机共振方法对多频信号检测的局限性,利用小波变换频段分离的特性,将小波变换与归一化随机共振相结合,进行多频微弱信号检测仿真实验。仿真结果表明,结合了小波变换的归一化随机共振的方案能够检测出待测频段内的多频微弱周期信号。     

Levy噪声下自适应级联三稳随机共振系统研究

在Levy噪声驱动下,以级联三稳态随机共振为模型,首先研究了不同特征参数、对称参数条件下输入信噪比随噪声强度D的变化;然后以谱峰值和平均信噪比增益为性能指标,针对高、低频信号级联三稳随机共振现象进行了研究;最后将其应用到轴承故障检测中。研究表明,通过自适应算法选取最优参数a、b和c,可诱导级联三稳系统发生随机共振,从而实现对目标信号的检测。结果表明,特征参数越大,输入信噪比达到稳定状态需要的噪声强度越大,而对称参数几乎没有影响;选取相同的系统参数a=0. 5、b=0. 5、c=0. 8时,级联三稳系统的检测效果要比单级三稳系统有更好的随机共振输出,在小参数信号检测中,单级输出平均信噪比增益为25. 46 d B,第2级输出平均信噪比增益为28. 38 d B。此外,在轴承故障检测中级联三稳系统也显示出更好的检测效果。此系统对于微弱信号的检测具有重要的研究意义也有着良好发展前景。     

随机共振的两种仿真方法研究

介绍了利用随机共振效应检测微弱信号的原理,分别通过Matlab编程和Simulink建模研究了系统的随机共振现象。两种仿真结果表明,随机共振能有效地实现微弱周期信号的检测,并且提取出信号的频率。对于强噪声背景下的弱周期信号的检测,Simulink建模方法具有很大的优越性,建模简单且幅值失真较轻。     

基于混沌理论的电力系统微弱谐波检测方法研究

针对微弱谐波信号时频域分析检测方法在宽谱环境噪声较强场景中无法识别目标信号的缺陷,首先引入混沌系统的随机共振原理,在信号成分完全未知的情况下,利用随机噪声增强微弱目标信号进行频率盲检。进一步引入Duffing混沌系统,利用其对特定频率周期信号幅值的极高敏感性和噪声抵抗力,对检出的各谐波频率分量进行幅值精准检测。在经典随机共振模型的基础上,通过归一化改进了系统参数。结合Duffing混沌系统模型,设计了实时检测控制策略。通过粒子群寻优算法,以系统输入输出信号的互相关系数为适应度函数,寻找最优系统参数。对强随机噪声背景下的多整次谐波与间谐波混叠的谐波信号进行了仿真实验。结果表明,所提方法能在恶劣的噪声环境中,实现各谐波分量的无差频率检测与高精度幅值检测。     

基于调制随机共振的转子故障早期检测

噪声是影响旋转机械早期故障检测的主要因素。只有抑制噪声增强信号、提高信噪比,才能从噪声中提取故障特征信息,为故障诊断特别是早期故障检测提供可靠的依据。文中根据非线性双稳系统在噪声和弱周期信号作用下的周期响应特性,将调制技术与随机共振原理相结合,提出了调制随机共振方法,实现了在较宽的频率范围内从强噪声中检测微弱周期信号。理论分析、数值仿真和实验结果表明,对信号进行调制产生的差频分量可形成低频信号,该低频信号通过双稳系统易产生随机共振,从而使随机共振发生在具有优良频率特性的低频区,能使微弱的故障信号特征突出、明显而易于捕捉。该方法灵活、可靠,在转子系统早期故障检测方面的应用是可行的。     

小波-EMD和随机共振级联微弱信号检测

针对低信噪比下微弱信号检测困难问题,提出了一种三级级联微弱信号联合检测系统;首先将含噪信号通过小波阈值降噪,然后将降噪后的信号进行经验模态分解(EMD),利用信噪比定位模态法选取信噪比较高模态分量,对选取的模态进行合成使其待测特征得到突出;最后将其送入欠阻尼三稳态系统,使用果蝇智能参数寻优算法将稳态系统达到最佳随机共振状态,便于检测结果精确可靠。经过单频小信号及混频大信号仿真分析和滚动轴承内外圈故障诊断表明,该系统能够使-30 dB左右微弱信号的信噪比至少增加20 dB,扩大了微弱信号的检测范围,提高了检测的有效性。     

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.     

An adaptive improved unsaturated bistable stochastic resonance method based on weak signal detection

Stochastic resonance can detect weak periodic signals from strong background noise without loss of signal energy. However, the classical bistable stochastic resonance has the inherent output saturation defect, which limits the detection performance of system. And it is more difficult to detect signal with strong background noise. In this article, we constructed improved unsaturated bistable stochastic resonance to overcome this shortcoming. The improved bistable potential function makes the output signal more easily oscillate in two potential wells. To improve the stability and the accuracy of the method, we further propose an adaptive improved unsaturated bistable stochastic resonance (AIUBSR) by constructing a synthetic index (SI). The SI combines zero-crossing ratio and structural correlation coefficient, which can measure the periodicity of output signal and the accuracy of detective frequency at the same time. Theoretical analysis and numerical simulations show that the proposed AIUBSR can have good weak signal detection capability in strong background noise.     

Frequency modulated weak signal detection based on stochastic resonance and genetic algorithm

Stochastic resonance system is subject to the restriction of small frequency parameter in weak signal detection, in order to solve this problem, a frequency modulated weak signal detection method based on stochastic resonance and genetic algorithm is presented in this paper. The frequency limit of stochastic resonance is eliminated by introducing carrier signal, which is multiplied with the measured signal to be injected in the stochastic resonance system, meanwhile, using genetic algorithm to optimize the carrier signal frequency, which determine the generated difference-frequency signal in the low frequency range, so as to achieve the stochastic resonance weak signal detection. Results show that the proposed method is feasible and effective, which can significantly improve the output SNR of stochastic resonance, in addition, the system has the better self-adaptability, according to the operation result and output phenomenon, the unknown frequency of the signal to be measured can be obtained, so as to realize the weak signal detection of arbitrary frequency.     

微弱信号检测的3种非线性方法

阐述了随机共振法、混沌振子法及差分振子法3种方法的基本监测思想,确定了数学模型。随机共振系统(SR)是一个非线性双稳态系统,存在着在某一最佳输入噪声强度下,使系统产生最高信噪比输出,达到抑制噪声、放大微弱信号的目的。SR系统数学模型可由非线性Langevin方程定义,并进行了分析及仿真。混沌系统具有对初值敏感性及对噪声免疫的特点,数学模型选用Holmes型Duffing方程为监测器,进行了分析及仿真。差分振子法是基于差分方程构造监测器,以二维离散性系统作为数学模型,进行了分析及仿真。将3种方法应用于同步发电机转子匝间短路故障监测和异步电动机转子断条故障监测的实例中是成功的,证明了3种方法的有效性和准确性。     

Adaptive time‐frequency representation for weak chirp signals based on Duffing oscillator stopping oscillation system

To investigate high‐resolution time‐frequency representations for any type of weak chirp signals with a very low signal‐noise ratio, we revisit the inherent deficiencies of conventional Duffing oscillator detection methods and propose a novel Duffing oscillator stopping oscillation detection system. As a result, the detection of chirp signals can be successfully realized, and the influence of nondetection zones and critical thresholds on the detection accuracy is successfully eliminated. Furthermore, we propose an adaptive Duffing oscillator stopping oscillation detection method to measure the instantaneous frequency variation of a highly dynamic chirp signal within a large frequency range. The simulation results indicate that, compared with the conventional Duffing oscillator detection methods and the Choi‐Williams distribution, the proposed method greatly expands the frequency detection range of a single Duffing oscillator and has a lower computing cost and effective real‐time performance in detecting a high‐precision weak chirp signal, which provides a new solution for the time‐frequency representation of weak chirp signals at a lower signal‐noise ration and reveals broad prospects for applications in engineering.     

Levy噪声中EMD降噪的随机共振研究

经验模态分解(empirical mode decomposition,EMD)降低噪声的同时也削弱信号能量,并会产生虚假信号,导致信号检测存在缺陷,针对这一问题,提出Levy噪声环境下经验模态分解随机共振检测方法。通过将含噪信号进行EMD分解,对分解后信号进行叠加取平均二次采样等处理方法,使其满足随机共振要求,利用自适应算法优化系统参数,进而使处理后信号能够在双稳系统中产生随机共振,达到精确检测的目的。理论分析及实验证明在Levy噪声下,此方法能实现同一特征指数下单频信号与多频信号检测,实验表明在单频信号信噪比为-28 dB情况下能有14 dB的提高,特征指数为1.8下多频信号5 Hz频谱幅值从311.8增加到724,10 Hz频谱幅值由138.9增加到143.2。此方法对在复杂噪声环境中降低剩余噪声能量同时,提高信号能量,减少虚假信号,相对于仅仅进行EMD分解无法判断信号成分,能更好的达到检测效果。     

Adaptive filtering‐based multi‐innovation gradient algorithm for input nonlinear systems with autoregressive noise

In this paper, by means of the adaptive filtering technique and the multi‐innovation identification theory, an adaptive filtering‐based multi‐innovation stochastic gradient identification algorithm is derived for Hammerstein nonlinear systems with colored noise. The new adaptive filtering configuration consists of a noise whitening filter and a parameter estimator. The simulation results show that the proposed algorithm has higher parameter estimation accuracies and faster convergence rates than the multi‐innovation stochastic gradient algorithm for the same innovation length. As the innovation length increases, the filtering‐based multi‐innovation stochastic gradient algorithm gives smaller parameter estimation errors than the recursive least squares algorithm.     

基于Hilbert的单边带调制随机共振的微弱信号检测

为了克服经典随机共振中的小参数检测条件的限制,介绍了基于Hilbert变换的单边带频率调制技术,并与二阶随机共振系统相结合,提出了运用调制随机共振的方法实现工程中大信号检测的应用。针对小采样频率和大采样频率进行了分开讨论,并对基频信号的选取做了相关研究。研究结果发现,小采样频率下,Hilbert单边带频率调制技术结合二阶系统有很好的检测效果。大采样频率下,可以结合变尺度处理进行优化。数值仿真分析表明,基频信号取在频率轴分辨率的10～60倍时,会有一个较高的并且较稳定的输出信噪比。并将变尺度Hilbert单边带频率调制技术运用于实际的轴承内外圈故障信号检测中,能明显、准确的检测出单频故障大信号。