基于双共振的微弱信号检测方法与试验研究

将两个单一双稳系统经非线性耦合而成耦合双稳系统,其中一双稳系统为被控系统,受到弱输入周期信号作用;另一双稳系统为控制系统,受到控制信号的作用。分析控制信号作用下的耦合双稳系统的双共振特性,当控制信号频率两倍于弱输入周期信号频率时,耦合双稳系统中发生控制系统中的共振与被控系统的随机共振。提出基于双共振的随机共振增强方法,并设计耦合双稳系统双共振硬件电路,电路系统试验结果证实了耦合双稳系统中存在着双共振,且能有效增强被控系统中的随机共振。将该双共振电路系统应用于实际轴承故障信号检测中,系统输出功率谱在特征频率处的谱值大幅增强,可准确检测出强噪声背景中的微弱轴承故障信号,为基于随机共振原理的信号检测电路设计提供一种新方案,具有良好的应用前景。     

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。     

级联分段线性随机共振的微弱信号检测

针对强背景噪声下微弱信号检测困难的问题,提出了一种级联分段线性随机共振的微弱信号增强检测方法。该方法采用分段线性随机共振模型,避免了经典双稳系统对强噪声下弱信号提取时存在的饱和现象,同时,选用的分段线性系统的级联方式可使高频噪声被有效滤掉,低频信号能量不断增强。仿真信号和滚动轴承故障信号的检测结果表明,该方法可以适应更低信噪比信号的检测,参数调节方便,检测结果优于级联双稳系统,具有良好的工程应用前景。     

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。     

基于时延约束势随机共振的机械故障诊断方法研究

在实际工作环境中,机械设备的有用信号通常很微弱,并会被淹没在强噪声中,导致其故障特征很难被提取出来,针对这一问题,提出了一种基于时延约束势随机共振的机械故障信号检测方法。首先,建立了时延约束势随机共振模型,描述了其势函数的结构和功能特点,从理论上推导了输出信噪比的数学表达式,并研究了系统参数、时延长度和反馈强度对信噪比和噪声强度关系的影响;然后,利用蚁群算法的参数优化能力,实现了随机共振系统的最佳匹配;最后,将提出的方法应用于仿真故障信号和实际滚动轴承的外圈故障信号的诊断实验中,并将结果与双稳态随机共振方法获得的结果进行了对比。研究结果表明:在故障频率为60 Hz和143.08 Hz时,相比于经典的双稳态随机共振方法,所提出的时延约束势随机共振方法具有更高的频谱峰值,并且其受噪声干扰较小,故障识别效果更明显;该结果可以提高滚动轴承等机械设备的微弱故障诊断能力。     

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。     

基于随机共振的周期弱信号检测方法研究

实际工程中有很多被噪声淹没的信号需要检测,如航空发动机转子振动信号,滚动轴承与齿轮故障信号等。这些待测信号的检测准确度直接影响了工程技术的可靠性。随机共振是一种新型的信号检测方法,通过噪声刺激和系统参数刺激可实现系统共振的产生,从而将噪声的部分能量转移给信号,实现信号检测。根据齿轮均匀磨损后引起的低频振动信号与正弦信号十分接近的特征信息,本文取正弦信号模拟齿轮故障振动信号,以MATLAB为仿真平台,研究强噪声背景下的周期故障信号随机共振检测方法,为随机共振检测方法在故障检测领域的进一步应用奠定了基础。     

基于DSP的随机共振检测系统

以TMS320VC33为随机共振检测系统处理核心,介绍了强噪声背景下检测微弱信号的随机共振技术,TMS320VC33的性能特点,基于CPLD的自动数据采集电路,与PCI桥CY7C09449的接口,系统的软硬件设计与调试.该系统在电源设备故障信号检测应用中取得了很好的效果.     

时延反馈EVG系统随机共振特性研究及轴承故障诊断

随机共振是应用在微弱信号检测中的一种重要的技术,以微弱周期信号和加性高斯白噪声驱动的时延反馈生态植被生长(EVG)系统为模型,对其展开了详细的随机共振现象分析,并将其应用到微弱信号检测和轴承故障诊断中。首先,利用福柯普朗克方程推算出等效势函数以进一步得到系统信噪比的表达式,然后通过曲线图具体分析不同的系统参数对势函数和信噪比的影响。研究结果表明,通过调节系统参数、信号幅值、噪声强度均可诱导时延反馈EVG系统产生随机共振现象。最后,通过调节参数利用时延反馈EVG系统随机共振方法成功检测到微弱信号目标频率f=0. 01 Hz幅值为2 978,且在轴承内、外圈故障特征频率处检测出明显的峰值。     

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。     

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.     

Weak feature enhancement in machinery fault diagnosis using empirical wavelet transform and an improved adaptive bistable stochastic resonance

Machinery vibration signal is a typical multi-component signal and fault features are often submerged by some interference components. To accurately extract fault features, a weak feature enhancement method based on empirical wavelet transform (EWT) and an improved adaptive bistable stochastic resonance (IABSR) is proposed. This method makes full use of the signal decomposition performance of EWT and the signal enhancement of the IABSR to achieve the purpose of fault feature enhancement in low frequency band of FFT spectrum. Firstly, EWT is used as the preprocessing program of bistable stochastic resonance (BSR) to decompose the machinery vibration signal into a set of sub-components. Then, the sensitive component that contains main fault information is further input into BSR system to enhance fault features with the assistance of residual noises. Finally, the fault features are identified from fast Fourier transform (FFT) spectrum of the BSR output. To achieve the optimal BSR output, the IABSR method based on salp swarm algorithm (SSA) is presented. Compared with the tradition adaptive BSR (ABSR), the IABSR optimizes not only the BSR system parameters but also the calculation step size. Two case studies on machinery fault diagnosis demonstrate the effectiveness and superiority of the proposed method. In addition, the proposed method is easy to implement and is robust to noise to some extent.     

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.     

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.     

Weak fault feature extraction of rolling bearing under strong poisson noise and variable speed conditions

Fault feature extraction of the rolling bearing under strong background noise is always a difficult problem in bearing fault diagnosis. At present, most of the research focuses on weak signal extraction under Gaussian white noise and has certain practical significance. However, the noise in engineering is often complex and changeable, Gaussian white noise cannot fully simulate the actual strong background noise. Poisson white noise is a type of typical non-Gaussian noise, which widely exists in complex mechanical impact. It is of great significance to study the weak fault feature extraction of a faulty bearing under this type of noise. At the same time, variable speed conditions occupy most rotating machinery speed conditions. Non-stationary vibration signals make it difficult to extract fault features, and the frequency spectrum ambiguity will occur because of speed fluctuation. To solve the above problems, a method of weak feature extraction of a faulty bearing based on computed order analysis (COA) and adaptive stochastic resonance (SR) is proposed. Firstly, by numerical simulation, the non-stationary fault characteristic signal corrupted with strong Poisson noise is transformed into a stationary signal in the angle domain by COA. Secondly, the influence of the parameters of the pulse arrival rate and noise intensity of Poisson white noise on the optimal SR response in the angle domain are studied, and the influence of the parameters of Poisson white noise on the fault feature extraction is given. Then, adaptive SR method is used to extract and enhance fault feature information. Finally, the effectiveness of this method in weak fault characteristic signal extraction under strong Poisson noise is verified by experiments. Numerical simulation and experimental results verify the effectiveness of the proposed method in bearing fault diagnosis under strong Poisson noise and variable speed conditions.