参数调节随机共振在机械系统早期故障检测中的应用

随机共振是一种利用噪声使微弱信号增强传输的非线性现象,与线性方法相比能够检测更低信噪比的信号.为了准确捕捉表征机械早期故障的特征信号,在分析双稳系统参数及检测信号幅值对随机共振检测性能影响规律基础上,以输入信噪比为变量、信噪比增益为信号增强程度衡量指标,提出一种自适应调节系统参数的随机共振微弱信号检测新方法,讨论了该方法的基本原理及实现步骤.将该方法用于转子碰摩故障早期检测,结果表明该方法简单稳健、实时性好,在短数据条件下能把信噪比较低的周期信号从强背景噪声中可靠地提取出来.     

频率调制随机共振的数值分析

对随机共振技术运用于强噪声背景下的弱信号检测进行了研究,提出了用频率调制的方法,实现了在大参数情况下从强噪声中检测微弱周期信号.数值计算结果表明,该方法可形成低频信号,该低频信号通过双稳系统易产生随机共振,能使微弱的故障信号特征突出、明显,易于捕捉.     

基于LabVIEW的随机共振高频微弱信号检测系统

针对经典随机共振方法对高频微弱信号检测失效的难题,提出一种调参随机共振检测高频微弱信号的方法,并以LabVIEW和Matlab为开发平台,利用调参随机共振方法构建了检测无线电高频微弱信号系统。该检测系统能够根据待测信号的特征,通过调节系统参数诱发系统发生随机共振,从而实现对高频信号的检测。最后对实际中无线电含噪信号进行检测,实验结果表明,该系统人机界面友好,能够有效地检测出强噪声背景下的高频微弱信号,具有良好的可操作性和现实意义。     

耦合随机共振阵列集合平均增强效应

基于双稳态随机共振系统,利用随机共振的阵列增强效应,将多个随机共振子相互耦合连接,从而得到随机共振阵列模型。并将集合平均的思想应用到模型当中,使模型的信号增强效应得到进一步提高。在此基础上将耦合随机共振阵列模型与单个随机共振子模型进行了微弱信号检测性能比较,仿真结果以及对轴承故障信号检测的结果表明,此方法可以准确检测出强噪声背景下的微弱周期信号,相比于单个SR振子模型,输出信噪比增益显著大于1,信号增强效果更加显著,有利于在信号检测领域推广应用。     

基于幂函数型双稳随机共振的故障信号检测方法

在实际的故障诊断中,有用信号经常淹没在噪声中,特征信息提取非常困难。为了提取强噪声背景中的微弱信号,将幂函数型单势阱模型与Gaussian Potential模型相结合提出一种新型的双稳随机共振系统,称为幂函数型双稳随机共振系统。首先,以平均信噪比增益为衡量指标,提出一种寻找最优系统参数组合的算法,使微弱信号、噪声及系统产生最佳的共振效果;然后,基于幂函数型双稳随机共振系统对Levy噪声背景下的仿真信号进行检测;最后提出一种基于小波变换和幂函数型双稳随机共振的微弱信号检测方法并应用于轴承故障信号检测中。仿真实验表明,幂函数型双稳随机共振模型在故障信号检测中是有效和可靠的。     

基于tsPSO算法的阵列自适应随机共振方法研究

针对传统随机共振方法存在的单级自适应随机共振方法输出响应信噪比低、参数自适应时间长且阵列随机共振方法参数设置困难等不足,提出了一种基于带极值扰动的简化粒子群(Extremum Disturbed and Simple Particle Swarm Optimization,tsPSO)算法的阵列自适应随机共振方法,实现了强噪声背景下大参数微弱信号的有效、快速检测。首先,采用并联随机共振系统,通过对各子系统的输出响应进行自相关分析并合成提高最终输出响应的信噪比;其次,在每个并联子系统中,通过随机共振系统级联的方式进一步提高输出响应的信噪比;最后,以信噪比为适应度函数,对每个子系统的参数进行自适应选择,并通过变换尺度分段搜索和采用ts PSO算法缩短参数自适应的时间。仿真实验和工程应用结果验证了该方法的有效性。     

基于变尺度随机共振的弱周期性冲击信号的检测

以绝热近似小参数的随机共振理论为依据，采用变尺度的方法实现大参数条件下的随机共振。通过调整变尺度随机共振Langevin方程的参数，成功地在强噪声背景下检测出微弱的周期性冲击信号。实验结果表明，该方法在回转类机械的振动信号分析中具有重要的实用价值。     

基于PSO和MSR的微弱信号检测方法研究

针对强噪声环境下,旋转机械系统的微弱信号难以得到准确检测的问题,提出了一种基于自适应权重粒子群算法(APSO)和自适应多稳态随机共振(SMSR)相结合的微弱信号检测方法.首先,使用自适应多稳态随机共振作为基本检测方法,并在数值求解输出信号时,引入了二次采样法(TS),解决了随机共振对高频信号适应能力差的问题;然后,以输...     

基于多稳态随机共振的轴承微弱故障信号检测

针对双稳态随机共振模型无法有效处理调制信号的缺点,提出了一种以包络信号为输入信号的自适应多稳态级联随机共振(adaptive multi-stable cascaded stochastic resonance,简称AMCSR)信号强化方法。首先,对振动信号进行包络解调,依据包络信号分布特点,选用与信号分布相匹配的多稳态随机共振模型;然后,以故障特征频率的频谱幅值为指标,采用蚁群算法自适应地优化随机共振模型参数;最后,以噪声为强化源和驱动信号,通过级联随机共振方法对包络信号中的故障特征频率进行逐级强化,获得故障特征成分的强化信号。对实测轴承振动信号的验证结果表明,该方法能够增强故障特征频率成分,有效地提取被其他频率成分淹没的微弱故障信号。     

采用知识的粒子群算法的多频微弱信号自适应随机共振检测方法

调参随机共振系统结构参数的选择对该检测方法的性能优劣起着决定性的作用。针对工程应用中对多频微弱信号实时检测的要求,提出以平均输出信噪比为适应度函数,将随机共振系统产生最佳共振效应时势垒与噪声强度大致相等这一特性作为知识,采用基于知识的粒子群算法来并行优化随机共振系统结构参数。与标准粒子群算法相比,该算法能以更快的速度得到最佳的系统结构参数,自适应地实现非线性系统、输入信号和噪声之间的最佳匹配,削弱多频含噪信号中的噪声,提高信号的输出信噪比。仿真试验和水轮机振动信号提取的工程应用均表明,该方法参数寻优效率高,简单易行,在采样点数较少的条件下能最优地检测出淹没在强噪声中的多频微弱信号,可以实现早期故障特征信号的提取。     

Engineering signal processing based on bistable stochastic resonance

The stochastic resonance (SR) characteristics of a single bistable system and two bistable systems connected in series with small and large parameters have been investigated, respectively. The viewpoint is that a single bistable system is better than a cascaded bistable system in detecting a weak periodic signal in frequency domain, and that, in time field, a cascaded system can detect a more beautiful waveform of either a periodic or an aperiodic weak signal. However, for some detection of a special signal, the periodic pulse for instance, a single bistable system is of great benefit to the signal extraction in time domain. It can provide some important information properties that are hardly obtained in frequency spectrum. Two examples of detecting a weak signal embedded in strong noise are presented in the end to illustrate that a single bistable system and a cascaded bistable system are both powerful tools for signal processing.     

Study on multi-frequency weak signal detection method based on stochastic resonance tuning by multi-scale noise

In practical engineering applications, useful information is often submerged in strong noise and the feature information is difficult to be extracted. Aimed at the detection problem of multi-frequency signal under colored noise background, a novel weak signal detection method based on stochastic resonance (SR) tuning by multi-scale noise is proposed. Firstly, noisy signal is processed by orthogonal wavelet transform to decompose the signal into multi-scale ingredients. According to the orthogonal wavelet transform coefficients characteristics of 1/f distribution, multi-scale noise is constructed so as to make the frequency-band containing the driving frequency be enhanced through SR system. Thus multi-frequency weak signal is detected. The method is effective to detect multi-frequency weak signal under colored noise background. Experiment signal analysis results show that the proposed method is simple for multi-frequency weak signal detection, and has good prospects for engineering applications.     

基于多稳随机共振的机电设备故障诊断研究

针对机械轴承早期故障诊断提出了多稳随机共振检测方法。分析了系统参数对多稳系统结构的影响,研究了高斯噪声背景下基于多稳随机共振的微弱信号检测方法。采用平均输出信噪比作为衡量指标,以多频微弱信号为待测信号进行数值仿真,并将其应用于滚动轴承故障信号检测中,实验结果均表明,该方法对早期故障振动信号具备准确的诊断能力,为其应用于工程实践奠定了基础。     

Enhanced detection of rolling element bearing fault based on stochastic resonance

Early bearing faults can generate a series of weak impacts. All the influence factors in measurement may degrade the vibration signal. Currently, bearing fault enhanced detection method based on stochastic resonance(SR) is implemented by expensive computation and demands high sampling rate, which requires high quality software and hardware for fault diagnosis. In order to extract bearing characteristic frequencies component, SR normalized scale transform procedures are presented and a circuit module is designed based on parameter-tuning bistable SR. In the simulation test, discrete and analog sinusoidal signals under heavy noise are enhanced by SR normalized scale transform and circuit module respectively. Two bearing fault enhanced detection strategies are proposed. One is realized by pure computation with normalized scale transform for sampled vibration signal, and the other is carried out by designed SR hardware with circuit module for analog vibration signal directly. The first strategy is flexible for discrete signal processing, and the second strategy demands much lower sampling frequency and less computational cost. The application results of the two strategies on bearing inner race fault detection of a test rig show that the local signal to noise ratio of the characteristic components obtained by the proposed methods are enhanced by about 50% compared with the band pass envelope analysis for the bearing with weaker fault. In addition, helicopter transmission bearing fault detection validates the effectiveness of the enhanced detection strategy with hardware. The combination of SR normalized scale transform and circuit module can meet the need of different application fields or conditions, thus providing a practical scheme for enhanced detection of bearing fault.     

Study on non-linear filter characteristic and engineering application of cascaded bistable stochastic resonance system

This paper addresses the problem of cascaded bistable stochastic resonance system (CBSRS) with large parameters, and reveals its non-linear low-pass filter characteristic. The study results show that weak characteristic frequency component located in low-frequency area can be extracted gradually from strong noise background owing to the energy transfer mechanism from high-frequency area to low-frequency area, as a result, a novel low-pass filter can be achieved ultimately. Compared with conventional digital filter, low-pass filter based-on CBSRS has the advantage of extracting some certain weak low-frequency characteristic components while implementing low-pass filter. Simulated experiments and mechanical fault diagnosis examples are presented in order to demonstrate that CBSRS is a powerful tool for signal processing.     

自适应扫频随机共振研究

研究了小参数随机共振和大参数二次采样随机共振应用的局限性，提出自适应扫频随机共振算法，通过自动改变信号采样频率和调整双稳系统的结构参数，实现了强噪声中弱信号的检测，达到工程应用的目的。实验研究表明，自适应扫频随机共振技术可应用于工程实际。     

Extended stochastic resonance (SR) and its applications in weak mechanical signal processing

To catch symptoms of machine failure as early as possible, one of the most important strategies is to apply more progressive techniques during signal processing. This paper presents a method based on stochastic resonance (SR) to detect weak fault signal. First, a discrete model of a bistable system that can demonstrate SR is researched, and the stability condition for controlling the selection of model parameters of the discrete model and guarantee the solving convergence are established. Then, the frequency range of the weak signals that the SR model can detect is extended through a type of normalized scale transformation. Finally, the method is applied to extract the weak characteristic component from heavy noise to indicate the little crack fault in a bearing outer circle.