NEW TECHNOLOGY FOR FAULT DIAGNOSIS BASED ON WAVELET DENOISING AND MODIFIED EXPONENTIAL TIME-FREQUENCY DISTRIBUTION

Fast wavelet multi-resolution analysis (wavelet MRA) provides a effective tool for analyzing and canceling disturbing components in original signal. Because of its exponential frequency axis, this method isn't suitable for extracting harmonic components. The modified exponential time-frequency distribution ( MED) overcomes the problems of Wigner distribution( WD) ,can suppress cross-terms and cancel noise further more. MED provides high resolution in both time and frequency domains, so it can make out weak period impulse components fmm signal with mighty harmonic components. According to the 'time' behavior, together with 'frequency' behavior in one figure,the essential structure of a signal is revealed clearly. According to the analysis of algorithm and fault diagnosis example, the joint of wavelet MRA and MED is a powerful tool for fault diagnosis.

SAR image denoising based on wavelet-fractal analysis

Wavelet-fractal based SAR （synthetic aperture radar） image processing is one of the advanced technologies in image processing. The main concept of analysis is that after wavelet transformation, multifractal spectrum of the signal is different from that of noise. This difference is used to alleviate the noise produced by SAR image.The method to denoise SAR image using the process based on wavelet-fractai analysis is discussed in detail. Essentially, the present method focuses on adjusting the Hoelder exponent α of multifractal spectrum. After simulation, α should be adjusted to 1.72-1.73. The more the value of α exceeds 1.73, the less distinctive the edges of SAR image become. According to the authors denoising is optimal at α=1.72-1.73. In other words, when α =1.72-1.73, a smooth and denoised SAR image is produced.

FAULT DIAGNOSIS APPROACH FOR ROLLER BEARINGS BASED ON EMPIRICAL MODE DECOMPOSITION METHOD AND HILBERT TRANSFORM

Based upon empirical mode decomposition (EMD) method and Hilbert spectrum, a method for fault diagnosis of roller bearing is proposed. The orthogonal wavelet bases are used to translate vibration signals of a roller bearing into time-scale representation, then, an envelope signal can be obtained by envelope spectrum analysis of wavelet coefficients of high scales. By applying EMD method and Hilbert transform to the envelope signal, we can get the local Hilbert marginal spectrum from which the faults in a roller bearing can be diagnosed and fault patterns can be identified. Practical vibration signals measured from roller bearings with out-race faults or inner-race faults are analyzed by the proposed method. The results show that the proposed method is superior to the traditional envelope spectrum method in extracting the fault characteristics of roller bearings.

CONSTRUCTION METHODS AND ALGORITHM DESIGN OF WAVELET BASES

Based on the brief introduction of the principles of wavelet analysis, this paper gives a summary of several typical wavelet bases from the point of view of perfect reconstruction of signals and emphasizes that designing wavelet bases which are used to decompose the signal into a two-band form is equivalent to designing a two-band filter bank with perfect or nearly perfect property. The generating algorithm corresponding to Daubechies bases and some simulated results are also given in the paper.

Study on Nonlinear Characteristics of Freak-Wave Forces with Different Wave Steepness

The nonlinear wave forces on vertical cylinders induced by freak wave trains were experimentally investigated. A series of freak wave trains with different wave steepness were modeled in a wave flume. The corresponding wave forces on vertical cylinders of different diameters were measured. The experimental wave forces were also compared with the predicted results based on Morison formula. Particular attentions were paid to the effects of wave steepness on the dimensionless peak forces, asymmetry characteristics of the impact forces and high-frequency force components. Wavelet-based analysis methods were employed in revealing the local energy structures and quadratic phase coupling in the freak wave forces.

次同步振荡在交直流电网中传播的关键影响因素

随着“双高”电力系统的发展,次同步振荡问题日益凸出,亟需研究交直流线路次同步振荡传播的关键影响因素。从系统响应量测时序数据着手,提出了一种次同步振荡传播关键影响因素定量分析方法。首先,基于自适应噪声完全集合经验模态分解(complete ensemble empirical mode decomposition, CEEMDAN)的改进小波阈值去噪方法对量测数据进行降噪处理,减少噪声对Prony分析的影响;其次,基于次同步振荡传播各影响因素的相关系数和互信息量建立相关性评价组合模型;最后,计算交直流不同参数在综合模型中的评价指标,得出次同步振荡在交直流线路中传播的关键影响因素。通过在PSCAD搭建2区域4机系统进行分析,结果表明:影响交流线路次同步振荡传播的极强相关参数为交流线路潮流,影响直流线路次同步振荡传播的极强相关参数为次同步振荡频率下交流线路阻抗特性。

DT-CWT-LMS自适应降噪模型构建及其在隧道监测工程中的应用

针对隧道工程监测现场应用布里渊光时域反射仪(BOTDR)系统存在的问题,提出基于双树复小波变换(DT-CWT)和改进的LMS算法的组合降噪模型,用于对BOTDR分布式光纤监测信号进行降噪处理。首先,基于双树复小波变换算法分解原始信号;然后,使用样本熵作为目标函数自动选择最优小波分解层数的模型,并基于优化双曲余弦函数改进LMS算法的收敛速度和收敛性;最后,为验证所提出算法的有效性,进行BOTDR温度信号降噪试验。试验结果表明:1)DT-CWT-LMS算法的降噪效果明显优于传统的小波阈值降噪方法,在6个温度梯度上的平均SNR值比WDD、EMT、EMD分别高出43.98%、17.5%、8.4%,平均RMSE值分别降低33.18%、17.14%、9.23%,平均SE值分别降低29.04%、21.17%、20.67%。为验证所提算法在工程现场的有效性,依托北京地铁隧道监测项目,使用DT-CWT-LMS算法对光纤监测信号进行降噪研究,降噪后信号的样本熵平均降低幅为64.03%;2)和常规WDD、EMT、EMD 3种方法相比,DT-CWT-LMS算法在3条光纤上的表现均优于其他3种算法,在1号、2号、3号光纤上的SNR指标比其他3种算法的平均值分别高出22%、38%、27%,说明该算法可作为隧道工程光纤监测信号的一种有效降噪方法。

一种基于小波-Contourlet变换的图像去噪算法

提出了一种基于小波-Contourlet变换的图像去噪算法.实验证明,该算法相对于小波变换和Contourlet变换能更稀疏的表达图像,并利用此优越性进行图像去噪,可以达到更好的效果和更高的PSNR值.

基于小波变换和Prony算法的间谐波参数辨识

现场采样获得的间谐波信号通常叠加着随机噪声,如果不对噪声进行有效消除,其信号特征信息的提取将会受到很大影响.通过对小波软阈值去噪方法的探讨,对间谐波信号进行去噪预处理,并采用现代谱估计P rony方法辨识间谐波参数,提取信号特征信息.计算结果表明,采用小波软阈值方法对间谐波信号进行去噪预处理,有利于提高其参数辨识的准确性.

强噪声中检测微弱目标信号特征的量子信号处理算法

随着噪声功率的增强,微弱目标信号的特征受噪声污染变得模糊且难以区分,导致微弱信号检测算法失效,提出一种可以保护目标信号特征的量子信号处理方法——局域半经典信号分析算法。详细介绍了算法实现量子化的原理和在量子域中保护目标信号特征的性质;给出算法步骤以及重要参数的计算方式;将所提算法与奇异值分解、小波阈值降噪算法结合进行了仿真分析和实验验证。结果表明,所提算法保护目标信号特征的能力可以帮助降噪算法检测极低信噪比的微弱信号,与其他方法结合可极大改善信噪比,准确提取信噪比为-30 dB的微弱目标信号,算法性能优越。

基于VMD和小波分析的油液磨粒信号去噪方法

油液金属磨粒检测传感器通过监测机械设备油路中的金属磨粒,可实时反馈机械设备故障特征。为了提升油液磨粒检测传感器的检测精度,文章提出一种针对油液磨粒信号的变分模态分解(Variational Mode Decomposition,VMD)结合小波分析的去噪方法。首先,通过计算各模态分量与原始油液磨粒信号的相关系数确定最优K值;其次,对原始信号进行VMD分解,筛选出特征分量;最后,利用小波阈值去噪方法对特征分量进行降噪处理。实验结果表明,与经验模态分解(Empirical Mode Decomposition,EMD)和传统小波去噪方法相比,本方法的信噪比最高,均方根误差最小,能量占比最大,在油液磨粒信号降噪效果中表现最好,有利于提升磨粒检测传感器的检测精度。

基于小波去噪和时频分析的智能电表量测数据挖掘研究

为提升智能电表量测数据挖掘效果,该文研究一种基于小波去噪和时频分析的智能电表量测数据挖掘方法。应用小波变换阈值去噪方法对数据集进行去噪,通过惩罚策略选择阈值后,对存在噪声的数据集进行运算,获取去噪后的数据集,根据去噪后的小波系数与存在噪声的小波系数获取最优阈值函数;利用自适应最优径向高斯核时频分析方法,有效将最优阈值函数的数据集分离为自分量信号与互分量信号,精准挖掘智能电表仿真模型数据库内数据,完成智能电表量测数据信息的输出。实验结果表明,所研究方法去噪效果较好,相对误差保持在2%以内,挖掘精度维持在96%以上,应用性能较好。

基于离散小波变换和GRU的触电诊断分析

[目的]在低压配电网中,作为用电安全的一种重要保障,剩余电流保护装置可减小用电器发生漏电故障而带来的危害,还可预防人体触电事故的发生。当前剩余电流保护装置依靠剩余电流信号大小作为保护机构动作的依据,无法识别触电特征。针对这个问题,文章提出了1种基于小波分解降噪与GRU的低压配电网触电信号特征提取及触电诊断的方法。[方法]文章对触电实验采集的剩余电流进行降采样和离散小波降噪等预处理;采用滑动窗口法提取剩余电流的时频域触电特征参数,利用傅里叶变换提取剩余电流对二次谐波幅值特征参数;提取的全部特征参数组成1个高维特征空间向量;采用主成分分析法对高维特征空间向量进行降维处理后得到1组新的三维特征向量;建立触电诊断模型,并将代表触电特征的三维特征向量作为该模型的输入量;运用门控循环网络(GRU)等5种不同的触电诊断模型对触电信号进行对比实验。[结果]实验结果表明:基于GRU的触电诊断模型的收敛效果较好,识别率达到98.33%。[结论]该方法对新一代的剩余电流保护装置的研究与开发提供了新的思路,为用电安全提供了有效保障。

小波包分析在大坝变形监测中的应用

为了更好地分析大坝在外界环境及自身振动下的变形状态,利用小波包分析在信号处理领域中的优势,将小波包分析应用于大坝变形监测数据处理中。首先对小波包的理论进行了介绍,然后研究并确定了实现监测数据小波包分析中的各种参数,如最优分解层次、最优小波包基及最优阈值估计准则,最后使用频谱分析对大坝监测数据进行频率分段,得到不同频段的频率及对应振幅,进一步分析了大坝的变形特征。

基于小波分析的湖南罗富冲滑坡监测数据去噪与预警

滑坡自动化监测数据中噪声的存在导致滑坡预警误报严重,在预警之前有必要对原始监测数据进行去噪。以湖南罗富冲滑坡为研究案例,首先使用小波分析对该滑坡GNSS地表变形数据进行分解,然后使用分解的低频分量进行信号重建以实现数据去噪,最后分别使用原始数据和去噪数据计算日变形速度以进行预警计算,通过对比分析验证去噪效果。研究结果表明,对原始滑坡自动化监测数据使用小波分析进行去噪处理能够有效提升预警的准确性,对于保障滑坡影响区域的人民群众生命财产安全具有重要意义。

GLOBAL MEASURE ON IMAGE CONTENT

This paper investigated approaches to supporting effective and efficient retrieval of image based on principle component analysis. First, it extracted the image content, texture and color. Gabor wavelet transforms were used to extract texture feature of the image and the average color was used to extract the color features. The principle component of the feature vector of image can be constructed. Content based image retrieval was performed by comparing the feature vector of the query image with the projection feature vector of the image database on the principle component space of the query image. By this technique, it can reduce the dimensionality of feature vector, which in turn reduce the searching time.

How soon would the next mega-earthquake occur in Japan?

The problem of seismic danger estimate in Japan after Tohoku mega-earthquake 11 March of 2011 is considered. The estimates are based on processing low-frequency seismic noise wave-forms from broadband network F-net. A new method of dynamic estimate of seismic danger is used for this problem. The method is based on calculating multi-fractal properties and minimum entropy of squared orthogonal wavelet coefficients for seismic noise. The analysis of the data using notion of “spots of seismic danger” shows that the seismic danger in Japan remains at high level after 2011. 03. 11 within north-east part of Philippine plate—at the region of Nankai Though which traditionally is regarded as the place of strongest earthquakes. It is well known that estimate of time moment of future shock is the most difficult problem in earthquake prediction. In this paper we try to find some peculiarities of the seismic noise data which could extract future danger time interval by analogy with the behavior before Tohoku earthquake. Two possible precursors of this type were found. They are the results of estimates within 1-year moving time window: based on correlation between 2 mean multi-fractal parameters of the noise and based on cluster analysis of annual clouds of 4 mean noise parameters. Both peculiarities of the noise data extract time interval 2013-2014 as the danger.

Real-time Virtual Environment Signal Extraction and Denoising Using Programmable Graphics Hardware

The sense of being within a three-dimensional （3D） space and interacting with virtual 3D objects in a computer-generated virtual environment （VE） often requires essential image, vision and sensor signal processing techniques such as differentiating and denoising. This paper describes novel implementations of the Gaussian filtering for characteristic signal extraction and waveletbased image denoising algorithms that run on the graphics processing unit （GPU）. While significant acceleration over standard CPU implementations is obtained through exploiting data parallelism provided by the modern programmable graphics hardware, the CPU can be freed up to run other computations more efficiently such as artificial intelligence （AI） and physics. The proposed GPU-based Gaussian filtering can extract surface information from a real object and provide its material features for rendering and illumination. The wavelet-based signal denoising for large size digital images realized in this project provided better realism for VE visualization without sacrificing real-time and interactive performances of an application.

融合改进小波去噪与T-Taylor的井下定位算法

针对超宽带井下定位方法中,信号易受到非视距(NLOS)误差的影响,导致定位算法的精确度与环境适应性较差的问题,本文提出了一种融合改进小波去噪和T-Taylor算法的井下定位算法。对原始测距值进行降噪处理,抑制NLOS误差对定位的影响;同时引入三球交会算法,将其作为Taylor算法的初始算法,确保Taylor算法收敛的同时,增强定位算法在井下复杂环境的定位精度与环境适应性;通过蒙特卡罗仿真试验进行验证。结果表明,融合算法能够在一定程度上降低测距误差,增强算法对于复杂环境的适应性,提高定位精度,在NLOS环境下较C-Taylor算法有更高的定位精度与抗噪声性能。经实地定位试验验证,融合算法平均定位精度相比于C-Taylor算法有一定程度的提升,提高了37.3%。