基于改进多路径匹配追踪的芯片超声信号去噪方法

针对高频超声检测倒装焊芯片微缺陷的回波信号受噪声影响的问题,提出了一种基于改进多路径匹配追踪算法(MMP)的高频超声信号稀疏去噪方法。利用MMP算法获取全局最优的原子,针对MMP计算量过大的问题,在迭代过程中设置阈值和引入剪枝操作,筛选误差较大的路径,减少迭代路径,降低算法复杂度。为了避免字典维度上升导致的计算量过大,通过构建连续原子库对重构信号参数进行调整,最终实现芯片超声检测信号噪声的抑制。通过仿真和实验证明,提出的方法能够有效的去除倒装芯片高频超声检测信号中的噪音,与其他去噪算法相比,所提方法通过增加少量的计算,实现信号重构精度的提高,提升了B扫图的清晰度。     

On-site non-destructive measurement of termite activity using the spectral kurtosis and the discrete wavelet transform

In this paper we present the operation results of a portable computer-based measurement equipment, conceived to perform non-destructive testing of suspected termite infestations. Its signal processing nucleus is the spectral kurtosis (SK), whose pattern allows the targeting of alarms and activity signals. Data have been also de-noised using the discrete wavelet transform (DWT) in order to study its potential complementarity to SK. The DWT keeps the successive approximations of the termite emissions, supposed more non-Gaussian (less noisy) and with less entropy than the detail coefficients. For a given mother wavelet, the maximum acceptable level in the wavelet decomposition tree, which preserves the insects’ emissions features, depends on the comparative evolution of the approximations vs. details’ entropies, and the value of the global SK associated to the approximation of the separated signals. The paper explains the detection criterion by showing different types of real-life recordings (alarms, activity, and background).     

Application of the L-Wigner distribution to the diagnosis of local defects of gear tooth

The detection of impulsive signals causing the fracture of gears is a significant task for the analysis of the characteristics of a damaged gear. However, this impulsive signal is hidden by background noise such as meshing frequencies and broadband noise. Recently, conventional time frequency methods have been used. In the case of a signal with a low SNR, these methods are not sufficient for the detection of impulsive signals; hence, the L-Wigner distribution with an S-method kernel is used and applied to the diagnosis of local defects of a tooth in a gear.     

Uncertainty handling in structural damage detection using fuzzy logic and probabilistic simulation

A fuzzy logic system (FLS) with a new sliding window defuzzifier is developed for damage detection. The effect of changes in the damage evaluation parameter (frequency) due to uncertainty in material properties is explored and the results of the probabilistic analysis are used to develop a robust FLS for damage detection. Probabilistic analysis is performed using Monte Carlo Simulation (MCS) on a beam finite element (FE) model to calculate statistical properties of the variation in natural frequencies of the beam due to structural damage and material uncertainty. Variation in these frequency measures, further contaminated with measurement noise, are used for testing the FLS. The FLS developed for damage detection in the steel beam having material uncertainty (elastic modulus) with coefficient of variation (COV) of 3 percent and noise level of 0.15 in the measurement data, correctly identifies the fault with an accuracy of about 94 percent. The FLS also accurately classifies the undamaged condition in presence of the mentioned uncertainties reducing the possibility of false alarms. From an algorithmic standpoint, this paper connects the disparate areas of probability and fuzzy logic to alleviate uncertainty issues in damage detection.     

Noise suppression and flaw detection of ultrasonic signals via empirical mode decomposition

In ultrasonic nondestructive testing, the precise detection of flaw echoes buried in backscattering noise caused by highly scattering materials is a problem of great importance. In this paper, a new signal decomposition method for analyzing nonstationary or nonlinear data, empirical mode decomposition, is proposed to deal with ultrasonic signals. A new denoising technique that combines empirical mode decomposition and filtering simultaneously in the time domain and frequency domain is designed to suppress noise and enhance flaw signals. Synthetic and experimental signals are denoised with this EMD-based filtering technique. Simulated results are presented and analyzed, showing that the proposed technique has an excellent performance even when the signal-to-noise ratio is very low (−23 dB). The improvement in flaw detection was experimentally verified on a pipeline sample with artificial flaws. The text was submitted by the authors in English.     

一种MCG信号的自适应预测滤波方法

与心电圈相比较,心磁检测具有无需电极,对某些局部心肌电流高度敏感,可以用于早期心脏疾病的诊断。但心脏磁场信号在检测过程中会被噪声所污染,使得信号本身的可辨识性降低,因此需要对该信号进行降噪处理。在单通道信号采集系统无噪声参考输入端的情况下采用自适应滤波方法,需要对待处理信号进行线性预测,本文提出的改进LMS（Least Mean Square）算法的自适应预测滤波器,无需噪声参考信号即可对心磁信号进．彳亍滤波,通过三种不同噪声的滤波仿真结果可见,采用自适应预测滤波器处理后明显提高了信噪比,具有一定的学术意义和实用价值。     

A new method for grinder dressing fault mitigation using real-time peak detection

To facilitate the implementation of machine monitoring algorithms on the shop floor, signal processing and decision-making strategies must be developed, which account for the difficulties associated with monitoring a machine in an industrial environment. Therefore, this paper focuses on introducing a new method for dresser contact detection, which takes into account sensor and data acquisition system costs, computational limitations of an embedded detection system, and noise robustness issues associated with shop floor implementation. This paper also discusses the way in which the monitoring algorithm directly interfaces with the machine control in real time in order to ensure that system faults are avoided. Furthermore, the new algorithm is compared with more traditional methods for change detection by applying each algorithm to the signal output from a dresser horsepower sensor. In an industrial application, the new algorithm is shown to provide zero missed detections, zero false alarms, and a sufficiently fast response time.     

Fault diagnosis of a gearbox using the sliced Wigner fourth order time frequency method smoothed by a new kernel function

The detection of impulsive signals embedded in the broadband noise is useful for the fault diagnosis of a gearbox. The sliced Wigner fourth-order time frequency method (SWFOTFM) has been used for the detection of impulsive signals embedded in the broadband noise. However, one disadvantage of SWFOTFM is that the non-oscillating cross-terms cannot be smoothed by conventional kernel functions. In this paper, a new kernel function is developed to reduce the non-oscillation cross-terms. The SWFOTFM using the new kernel function is successfully applied to the fault diagnosis of a gearbox.     

基于Lorenz系统的微弱谐和信号检测

基于非共振参数激励混沌抑制原理,利用受控Lorenz系统实现强噪声背景下微弱谐和信号的检测。根据检测系统经平均法和重整化方法处理后的参数等效关系,确定使系统动力学行为由周期轨道突变为稳定平衡点的检测参数临界值。仿真结果表明此系统可以准确检测出强噪声背景下的微弱谐和信号。相比于现有的混沌振子检测方法,此方案可由理论分析得到参数阈值的准确范围,且判决准则简单,有利于实现自动检测。     

Adaptive noise cancellation based on EMD in water-supply pipeline leak detection

In water-supply pipeline leak detection and location, both the leak signals and blurred noises are closely related to the pipeline states and surroundings and most of the conventional noise-cancellation methods have to depend on the empirical parameters of either signals or noises. EMD (Empirical Mode Decomposition) is an adaptive signal decomposition method and is exclusive of base functions. A signal is decomposed into several IMFs (Intrinsic Mode Functions) in EMD, then the noise in a signal can be cancelled through removing uncorrelated IMFs. The existing EMD noise cancellation methods need to know the characteristics of either the wanted signal or the noise for rebuilding the noise-removed signal. However the characteristics of leak signals and noises are not fixed in various pipeline conditions, so the existing EMD noise cancellation methods can’t be directly applied in water-supply pipeline leak detection. This paper proposes an adaptive noise cancellation method based on EMD, in which the IMFs that don’t or less contain the components related to the leak can be removed through the cross-correlation between the IMFs and another signal collected at the either side of a suspect leak. In simulation analysis, the adaptive noise cancellation method can increase the SNRs (Signal to Noise Ratios) of leak signals as high as 16 dB. In processing practical pipeline vibro-acoustic signals, with the proposed method the peak of adaptive time delay estimate of leak signals, which determines the location of a leakage, becomes more distinguished, and thus the error of leakage location is improved.     

Method of Weak Signals Detection Based on Chaos Suppression in the Nonresonant Parametric Drive

The evolution of chaotic state of Lorenz system on the familiar parameter space orbit is analyzed.Based on the principle of chaos suppression with nonresonant parametric drive,the model of detecting weak periodic signals in strong noise is built.According to the parametric equivalent relationship obtained using averaging method and renormalization method,the critical values of detection parameters are determined,which lead to a sudden change of system dynamical behavior from periodic orbit to stable equilibrium point.Simulation results show that weak periodic signals in strong noise can be detected accurately with the proposed system.The method can obtain accurate range of parameter threshold through theoretical analysis,and the detection criterion is rather simple,which is more convenient for automatic detection.     

Reducing the uncertainty in real-time impedance measurements

This paper arises from the exigency of enhancing the performance of a VXI-based digital meter designed for real-time tracking of impedance. Specifically, the metrological characterization of the meter has put into evidence that the experienced measurement uncertainty is mostly due to wide-band noise corrupting the acquired signals. With the aim of reducing its noise susceptibility without compromising the measurement rate, two digital signal-processing solutions are proposed. The first one pre-processes the acquired signals by means of predictive digital filtering for improving zero-crossing detection and, consequently, granting more reliable phase displacement measurements. The second solution evaluates the inner product of the signals of interest in order to average noise during the course of measurement, thus limiting its effect on the overall uncertainty. Many impedance measurements are carried out to draw a proper comparison between the proposed solutions.     

DNA测序电泳荧光信号的小波去噪分析

在DNA荧光测序中,噪声影响分析的准确度和检出限。相比其他滤波方法,小波分析具有良好的时频域分辨特性。在小波去噪处理中,正确选择合适的小波基函数和去噪阈值直接关系到信号去噪处理的质量。通过对毛细管电泳(CE)荧光信号的仿真分析,结果表明:选择sym7小波基函数、分解层数(lev=5)与使用软阈值,可以有效去除CE荧光光谱信号的噪声,提高分析准确度。将其用于处理实际的DNA电泳荧光信号,效果良好。     

基于小波变换的微弱生命信号去噪问题研究

为了解决生命探测雷达回波中微弱生命信号提取难的问题,采用小波变换的阈值去噪法对强噪声背景下的微弱的人体心跳信号时域波形进行了提取,在MATLAB环境下,利用软件程序对实采的人体心跳信号进行去噪处理,得到了较好的人体心跳时域波形,验证了小波变换可以在生命探测雷达回波中微弱生命信号的提取时发挥重要作用。     

基于Duffing方程的齿轮故障检测方法研究

提出一种基于修正Duffing方程间歇混沌理论的弱信号检测新方法.在该检测方法中,当输入信号频率与系统激励频率之间存在微小偏差时系统输出为间歇混沌信号,且其频率偏差可由输出混沌信号的统计特性进行估计.数值仿真结果表明这种方法可以准确检测出信噪比很低的微弱正弦信号.最后,利用实验平台采集齿轮振动声信号数据,分别采用频谱分析法和混沌弱信号检测法对实验数据进行检测,结果表明混沌弱信号检测法具有更高的检测精度和更强的抗干扰能力.     

基于时频分析的移频轨道交通信号检测方法

针对传统方法对交通信号检测时,由于未能提取交通信号的时频特征,导致信号检测时存在检测精度低、检测误差大和噪声频率不稳定等问题,提出基于时频分析的移频轨道交通信号检测方法。首先利用时频分析法对移频轨道交通信号的时频特征进行提取;再基于提取的信号时频特征,利用信号的概率密度函数获取交通信号的信号双谱;最后利用卷积神经网络分类处理有双谱的交通信号,实现信号检测。实验结果表明,该方法检测信号时,检测精度高、检测误差小,以及噪声频率稳定。     

一种新型的基于相关特性的信号重构算法

提出了一种能有效补偿噪声影响的信号重构算法。在噪声背景下，要想准确地恢复那些发生了非线性畸变的信号，通常是很困难的。在许多信号重构的算法中，大多都忽略了噪声的影响。这样就丢失了许多有用信息，尤其在要求实时处理的情况下会使信号误差变大。鉴于此，文中将噪声的影响考虑进来，利用接收到的信号与处理过的输出信号的相关特性建立目标函数，通过调节相关系数使目标函数达到最小值并且一定程度上抑制了噪声，从而使原始信号得到相对准确的恢复。同时，文中的算法易于实现信号的实时处理。通过计算机仿真验证了文中该算法的有效性和可行性。提出的算法对精密仪器检测、通信系统、语音信号处理等领域具有一定的参考价值。     

Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis

Empirical mode decomposition (EMD) has been widely applied to analyze vibration signals behavior for bearing failures detection. Vibration signals are almost always non-stationary since bearings are inherently dynamic (e.g., speed and load condition change over time). By using EMD, the complicated non-stationary vibration signal is decomposed into a number of stationary intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. Bi-spectrum, a third-order statistic, helps to identify phase coupling effects, the bi-spectrum is theoretically zero for Gaussian noise and it is flat for non-Gaussian white noise, consequently the bi-spectrum analysis is insensitive to random noise, which are useful for detecting faults in induction machines. Utilizing the advantages of EMD and bi-spectrum, this article proposes a joint method for detecting such faults, called bi-spectrum based EMD (BSEMD). First, original vibration signals collected from accelerometers are decomposed by EMD and a set of IMFs is produced. Then, the IMF signals are analyzed via bi-spectrum to detect outer race bearing defects. The procedure is illustrated with the experimental bearing vibration data. The experimental results show that BSEMD techniques can effectively diagnosis bearing failures.     

基于经验与变分混合分解的超声回波信号噪声消除方法

超声缺陷检测结果易受超声回波信号中复杂噪声的干扰,为了提高超声缺陷检测的准确度,提出一种基于混合分解的 超声回波信号噪声消除方法。 采用经验模态分解算法结合相关系数指标对超声回波信号进行预处理,得到消除低频噪声分量 的超声回波预处理信号。 基于变分模态分解将该预处理信号分解为一系列窄带本征模态函数,引入互信息指标估计变分模态 分解的最优模态数量,并根据窄带本征模态函数与预处理信号的相关系数提取有用的模态分量,实现对超声回波信号去噪结果 的重构。 通过仿真和实测超声回波信号验证了本文方法的去噪性能,并与现有方法进行了对比。 结果表明,本文方法可同时消 除超声回波信号中的高频和低频噪声,在不同信噪比条件下 EMD、VMD 和本文方法去噪结果的 SNR 均值分别为 10. 01、9. 48 和 16. 09 dB,验证了本文方法对于超声回波信号噪声消除的优越性。     

一种用于超声信号降噪的非凸变量重叠 群稀疏变分方法

粗大晶粒产生的大量散射噪声而导致的超声检测信号信噪比低问题是粗晶结构超声检测面临的一大难题。针对现有稀疏降噪方法在波形失真和幅值衰减方面的不足,本文提出了一种基于非凸变量重叠群稀疏变分的超声信号降噪方法。基于含散射噪声的典型超声信号,分析了非凸变量重叠群稀疏变分方法的主要参数(如非凸变量函数类型、正则化参数和乘法因子等)对其降噪效果的影响,并确定了适合超声信号降噪处理的参数选择依据。在此基础上,将非凸变量重叠群稀疏变分方法应用于典型钢锭超声检测信号的降噪处理。结果表明,该方法能够很好剔除钢锭超声检测信号中的散射噪声,提高了钢锭超声全聚焦成像的信噪比6 dB以上,研究工作为粗晶材料超声检测作了有益探索。