谱相关密度分析在轴承点蚀故障诊断中的研究

利用谱相关密度提取轴承故障特征时需要在循环频率域和频率域上同时兼顾高分析带宽和高分辨率,从而使得该方法的计算量庞大,难以达到较高的分析精度.鉴于此,首次在循环平稳分析中引入解析的思想,利用解析形式的谱相关密度在循环频率域不存在高频特征的特点,提出运用时域选抽技术,在保证分辨率的同时降低分析带宽,减少计算量,从而得到更好的分析效果.本文以一般调幅信号解析形式的谱相关密度分析为基础,对滚动轴承点蚀故障模型进行了分析,推导了其谱相关密度分析的理论结果,给出具体的算法实现.仿真调幅模型和实际轴承故障信号,证实了理论分析的正确性和算法的可行性,同时也验证了谱相关密度分析对调幅特征的提取能力.     

多轴平稳非高斯随机振动试验控制方法

多轴向平稳非高斯随机振动控制试验能够对指定响应信号的时、频域特征进行同时控制。提出了一种快速生成具有指定功率谱密度、斜度和峭度的平稳非高斯随机振动信号的方法。通过频率采样方法将目标功率谱密度设计成滤波器,利用非线性变换方法获取非高斯随机信号,再将此信号经过设计的滤波器以获得满足要求的非高斯随机信号。该方法简单、快速并克服了传统非线性变换方法的缺点。进一步将此方法应用于三轴向平稳非高斯随机振动试验中,给出了三轴向非高斯随机振动控制的闭环均衡步骤,此方法能够同时对信号的功率谱自谱、相干系数、相位差以及斜度和峭度进行解耦控制。进行了三轴向平稳非高斯随机振动控制试验,三个方向上加速度信号的功率谱密度、斜度和峭度控制效果均令人满意,满足工程应用要求。     

时频面上基于瞬频估计的信号提取方法

针对多分量非平稳信号的噪声抑制和信号分离问题,提出了一种新的时频滤波法.采用FMmlet自适应分解将被分析信号在时频空间内展开,得到无交叉项干扰、时频聚集性很强,且能反映信号的线性或非线性结构的时频特性的时频分布.根据时频面内能量脊与瞬时频率的对应关系,采用谱峰检测和时频加窗轮流迭代的方法,估计出信号分量的瞬时频率.在瞬时频率精确估计的基础上,设计适当的时频滤波函数,对被分析信号的Wigner-Ville分布(WVD)进行时频加窗处理,得到单分量信号的修正WVD,然后采用 WVD反变换进行信号分量的时域重构,达到从非平稳信号中分离有用分量的目的.理论分析和仿真实验表明,利用该方法从复杂非线性时变信号中提取出的信号分量失真度小.该方法在非平稳信号的深层特征提取中具有良好的应用前景.     

高斯白噪声序列谱的统计特性及应用研究

本文系统地分析和归纳了平稳高斯白噪声序列离散频谱、离散幅度谱、离散功率谱的统计特性，并就其具体应用作了探讨。     

基于广义解调平滑能量分离算法的瞬时频率估计

平滑能量分离算法能够跟踪调幅调频信号的瞬时频率,结合广义解调和复解析Gabor滤波器的优点能够克服平滑能量分离算法只适用于单分量窄带信号以及对噪声敏感的局限性。理论分析了负频干扰对传统广义解调多分量分离方法的影响,在此基础上提出了一种新的基于广义解调的平滑能量分离算法,该方法利用广义解调将非平稳信号转化为准平稳信号,再通过复解析Gabor滤波器对其进行滤波以达到单分量信号分离的效果,分离出来的准平稳信号无需进行逆广义解调,直接采用平滑能量分离算法求取瞬时频率,经过频率补偿得到原始信号的瞬时频率。仿真和试验结果表明该方法能够克服负频率干扰,且比传统方法具有更高的解调精度,进一步扩大了平滑能量分离算法的应用范围。     

一种滚动轴承故障特征提取的新方法—谱相关密度

本文简要地介绍了谱相关密度的概念及其对调幅信号的解调作用,并用数值仿真进行验证。针对滚动轴承故障信号含有调幅成分的特点,提出并讨论了利用谱相关密度来提取滚动轴承的故障特征的方法;最后通过对实验数据的处理及分析,说明谱相关密度在提取信号特征方面的有效性。     

基于LMD的多尺度形态学在齿轮故障诊断中的应用

数学形态学是一种非线性、非平稳分析方法,具有很强的抑制脉冲干扰的能力,但滤除白噪声的能力却不行。针对这一不足,提出一种基于LMD的多尺度形态学解调方法。该方法是将多分量调频调幅故障信号分解为一系列单分量调频调幅信号(PF),实现对故障信号的降噪处理,同时还可以获得原始信号的全部调制信息。选取能量高的PF分量求和重构,再用多尺度形态学差值滤波器提取出故障信号的频率特征。通过仿真和齿轮故障模拟实验证实了该方法的有效性。     

时延自相关解调频原理及其在齿轮故障诊断中的应用

当齿轮发生故障时,调幅和调频现象往往同时存在,但是传统方法通常只适用与解调幅,而不能解调频。同时振动信号中常含有大量的噪声,严重的影响了解调结果。由于高延迟的自相关函数具有受噪声影响比较小的特点,已有文献用时延自相关法解调幅,但是用做解调频还鲜有研究。为此,从理论上分析了调频信号和调幅调频信号的自相关函数,得出调频信号的自相关函数是调幅信号,调频调幅信号的自相关函数仍然是调幅调频信号,且其调制频率不变。这样就从理论上说明了时延自相关解调法是解调频和解调频的通用方法。最后通过仿真实验和实例分析,表明了时延自相关解调法不仅能有效的解调频,而且具有较好的抗噪性。     

导电梁在磁场中的磁弹性随机振动

根据磁弹性基本理论和连续体的随机振动理论，得到了在外加磁场中通有随机电流的梁的磁弹性随机振动方程。给出了导电梁在耦合场中的洛伦兹力及力矩，并将洛沦兹力的耦合项假设为梁的一种阻尼，另一部分假设为随机分布载荷，对梁的位移响应进行了分析。以简支梁为例，分别得到了外加磁场中通入平稳和非平稳随机电流时简支梁的位移响应的均值、自相关函数、功率谱密度函数等数字特征。最后以一个通有理想白噪声平稳随机电流的简支梁为实际算例，对其位移响应的功率谱密度函数进行了计算。图形形状的改变表明了耦合项对梁的功率谱密度函数的影响，据此可知通过控制随机电流和磁场可以达到控制梁的随机振动的目的。     

用1(1/2)维谱结合小波包能量提取地震动信号特征

针对车辆（轮式车、履带式车）引起的地震动信号中,具有非平稳、非高斯性特征相互重叠的实际情况,研究了地面活动目标产生的地震动信号特性;从理论上说明了1(1/2)维谱可消除车辆引起的地震动信号中的高斯白噪声或有色噪声,在将[112]维谱分析和小波包能量谱相结合的基础上,提出一种特征提取方法,以便区分不同的车辆目标。在时频域构建以[112]维谱和小波包能量谱作为地震动信号的联合特征向量,建立以训练误差为目标的BP神经网络模式分类器;然后对两类车辆信号进行识别。地震动信号的车辆实测数据表明,该方法能够准确和有效地识别车辆引起的地震动信号。     

离散频谱分析比值校正法幅值和相位的抗噪性分析

谐波信号离散频谱分析的比值校正法(内插法)在无噪声时是一种准确的校正方法,只存在计算时的舍入误差,但在包含噪声尤其信噪比较低时,校正精度会有所下降,甚至误差很大.研究了比值校正法的幅值与相位加不同的窗函数及加性高斯白噪声时的统计方差公式,并通过不同信噪比下的仿真验证了其准确性.建议避免在归一化频率误差较低的情况下使用加矩形窗的比值校正法来校正相位.     

Correlation between upper and lower sidebands

Experimental measurements supported by a simple model show that the upper and lower phase modulation (PM) noise sidebands are always equal and 100% correlated, independent of the noise power originating from multiplicative or additive processes. Similarly, we show that the upper and lower amplitude modulation (AM) noise sidebands are also equal and 100% correlated, independent of the noise power originating from multiplicative or additive processes, Moreover, the single sideband (SSB) PM noise is always equal to one-half the total PM noise. The same is true for the AM noise. Although the upper and lower PM or AM noise sidebands are equal and correlated for broadband additive noise, the phase between the AM and the PM sidebands varies randomly with time. These conclusions still hold even when the RF noise sidebands are not symmetric about the carrier.     

Detection of signals at the output of gravity resonance antennas by estimation of the distribution parameters

The statistical problem of detection of a useful vector signal at the output of resonance spatially-separated gravitational antennas with overlapping directional patterns is considered. A method involving estimation of the distribution parameters of the additive noise at the output of an optimal linear system of whitewashing and matched filters is used to solve the problem. The additive noise is assumed to be a mixture of correlated Gaussian noise and rare random pseudo-gravitational signals (minimax detection criterion).     

Feature Extraction for Crack Detection in Rain Conditions

The solution of the features selection problem is critical for robust detection of crack signals in noisy environment, varying from short-time impulses such as raindrops to the wide-band white Gaussian noise. In this paper, two novel feature selection methods were used to reduce an initial set of 90 features, 67 estimated in the time domain and 23 in the frequency domain, decreasing significantly the memory requirements and the computational complexity of a Radial-Basis-Function (RBF) cracks detector. The evaluation process is carried out in a database including of more than 6000 cracks, raindrops and simultaneous crack and raindrops signals. Additive white Gaussian noise is used to distort the real signals at ?20 to 20 dB Signal to Noise Ratio (SNR). The experimental results show that the number of features can be reduced to approximately 25, without affecting the classification rate of cracks and raindrops in the noisy signals, if the SNR is better than 0 dB. In noise-free environment a classification rate of 91% for a single crack/raindrop event is achieved using only five features. A different set of five features reaches a rate of 85% at 10 dB SNR.     

Neural network surface acoustic wave RF signal processor for digital modulation recognition

An architecture of a surface acoustic wave (SAW) processor based on an artificial neural network is proposed for an automatic recognition of different types of digital passband modulation. Three feed-forward networks are trained to recognize filtered and unfiltered binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals, as well as unfiltered BPSK, QPSK, and 16 quadrature amplitude (16QAM) signals. Performance of the processor in the presence of additive white Gaussian noise (AWGN) is simulated. The influence of second-order effects in SAW devices, phase, and amplitude errors on the performance of the processor also is studied.     

SNR and SINR-based selection combining algorithms in the presence of arbitrarily distributed co-channel interferers

The performance of selection combining (SC) diversity in the presence of additive white Gaussian noise as well as multiple co-channel interfering signals over fading channels is analysed. The analysis considers two SC algorithms, namely, the signal-to-noise ratio-(SNR) based SC algorithm and the signal-to-interference-plus-noise ratio (SINR)-based SC algorithm. Closed-form expressions for the distribution of the resulting SINR and the outage probability performance are derived for both SC algorithms. The derived expressions are valid for arbitrary number of interfering signals, and when the desired signal replicas experience independent Rayleigh fading with generalised statistics. In addition, they are applicable for the general case when the interfering signals are subject to arbitrary fading models, which include the cases of identical, non-identical, correlated and mixed-mode fading scenarios. Numerical results are presented to examine the impact of different fading conditions and system parameters on the outage performance for both SC algorithms under consideration.     

基于凸包优化的盲源分离在图像去噪中的研究

目的针对高斯-脉冲混合噪声图像中难以有效去除大量奇异点或离群数据的问题,提出一种基于凸包优化的盲源分离方法来去除图像中的混合噪声。方法该方法把混合噪声和原图均看作未知的源信号,依据噪声图像中混合噪声与原图内容的加性关系建立盲源分离的模型,并利用凸包优化的方法构建源信号(凸包极点)的仿射包,然后通过最小化仿射包到凸包(噪声图像)上的投影误差,求解混合噪声和原图2个源信号,实现去噪混合噪声、复原原图的目的。结果实验结果发现,无论高斯-脉冲混合噪声强弱,该方法去噪复原后的峰值信噪比和平均结构相似性分别在39.9129 d B和0.9以上。结论由实验数据证实该方法可有效地从盲源分离的角度去除图像中高斯-脉冲混合噪声、复原原始图像。     

一种基于累积量的ESPRIT型盲波束形成算法

讨论一种用于非高斯信号源方位估计的盲波束形成算法。假设背景噪声是二阶统计未知的有色(空间相关)高斯噪声,基于“阵元对”模型的ESPRIT方位估计算法可以通过用累积量矩阵取代自相关矩阵完成信号空间的重建。经过这种处理,加性有色高斯噪声可被滤除,因此算法不需要相关噪声矩阵的知识。当加性噪声源是空间相关矩阵未知的有色高斯噪声时,计算机仿真比较了基于累积量的ESPRIT算法与基于二阶统计的ESPRIT算法的性能。     

Minimal probability of incorrect solutions in the resolution of gravitational signals

The lower bound of the probability of incorrect solutions in the statistical problem of recognition of gravitational and pseudo-gravitational signals at the output of a resonant gravitational antenna is calculated. The additive noise in the individual channels of the antenna array is assumed to be Gaussian.