A Dual Tree Complex Discrete Cosine Harmonic Wavelet Transform (ADCHWT) and Its Application to Signal/Image Denoising

A new simple and efficient dual tree analytic wavelet transform based on Discrete Cosine Harmonic Wavelet Transform DCHWT (ADCHWT) has been proposed and is applied for signal and image denoising. The analytic DCHWT has been realized by applying DCHWT to the original signal and its Hilbert transform. The shift invariance and the envelope extraction properties of the ADCHWT have been found to be very effective in denoising speech and image signals, compared to that of DCHWT.

Harmonic signal extraction from noisy chaotic interference based on synchrosqueezed wavelet transform

For the harmonic signal extraction from chaotic interference, a harmonic signal extraction method is proposed based on synchrosqueezed wavelet transform(SWT). First, the mixed signal of chaotic signal, harmonic signal, and noise is decomposed into a series of intrinsic mode-type functions by synchrosqueezed wavelet transform(SWT) then the instantaneous frequency of intrinsic mode-type functions is analyzed by using of Hilbert transform, and the harmonic extraction is realized. In experiments of harmonic signal extraction, the Duffing and Lorenz chaotic signals are selected as interference signal, and the mixed signal of chaotic signal and harmonic signal is added by Gauss white noises of different intensities.The experimental results show that when the white noise intensity is in a certain range, the extracting harmonic signals measured by the proposed SWT method have higher precision, the harmonic signal extraction effect is obviously superior to the classical empirical mode decomposition method.

Power System Harmonic Detection Using Frequency-Domain Interpolation Wavelet Transform

Aiming at harmonic detection, fast Fourier transform can only detect integer harmonics precisely, short time Fourier transform can detect non-integer harmonics with low resolution, and some former wavelet based methods have no aliasing-reduction scheme which result in low measurement precision and poor robustness. A frequency-domain interpolation algorithm to detect harmonics is proposed by choosing Shannon wavelet. Shannon wavelet is an orthogonal wavelet possessing best ideal frequency domain localization ability, it can restrict wavelet abasing but bring about Gibbs oscillation phenomenon simultaneously. An interpolation algorithm is developed to overcome this problem. Simulation reveals that the proposed method can effectively cancel aliasing, spectral leakage and Gibbs phenomenon, so it provides an effective means for power system harmonic analysis.

Demodulation Based on Harmonic Wavelet and Its Application into Rotary Machinery Fault Diagnosis

The harmonic wavelet transform（HWT） and its fast realization based on fast Fourier transform（FFT） are introduced. Its ability to maintain the same amplitude-frequency feature is revealed. A new method to construct the time-frequency（TF） spectrum of HWT is proposed, which makes the HWT TF spectrum able to correctly reflect the time-frequency-amplitude distribution of the signal. A new way to calculate the HWT coefficients is proposed. By zero padding the data taken out, the non-decimated coefficients of HWT are obtained. Theoretical analysis shows that the modulus of the coefficients obtained by the new calculation way and living at a certain scale are the envelope of the component in the corresponding frequency band. By taking the cross section of the new TF spectrum, the demodulation for the component at a certain frequency band can be realized. A comparison with the Hilbert demodulation combined with band-pass filtering is done, which indicates for multi-components, the method proposed here is more suitable since it realizes ideal band-pass filtering and avoids pass band selecting. In the end, it is applied to bearing and gearbox fault diagnosis, and the results reflect that it can effectively extract the fault features in the signal.

A Discrete Cosine Adaptive Harmonic Wavelet Packet and Its Application to Signal Compression

A new adaptive Packet algorithm based on Discrete Cosine harmonic wavelet transform (DCHWT), (DCAHWP) has been proposed. This is realized by the Discrete Cosine Harmonic Wavelet transform (DCHTWT) which exploits the good properties of DCT viz., energy compaction (low leakage), frequency resolution and computational simplicity due its real nature, compared to those of DFT and its harmonic wavelet version. Hence the proposed wavelet packet is advantageous both in terms of performance and computational efficiency compared to those of existing DFT harmonic wavelet packet. Further, the new DCAHWP also enjoys the desirable properties of a Harmonic wavelet transform over the time domain WT, viz., built in decimation without any explicit antialiasing filtering and easy interpolation by mere concatenation of different scales in frequency (DCT) domain with out any image rejection filter and with out laborious delay compensation required. Further, the compression by the proposed DCAHWP is much better compared to that by adaptive WP based on Daubechies-2 wavelet (DBAWP). For a compression factor (CF) of 1/8, the ratio of the percentage error energy by proposed DCAHWP to that by DBAWP is about 1/8 and 1/5 for considered 1-D signal and speech signal, respectively. Its compression performance is better than that of DCHWT, both for 1-D and 2-D signals. The improvement is more significant for signals with abrupt changes or images with rapid variations (textures). For compression factor of 1/8, the ratio of the percentage error energy by DCAHWP to that by DCHWT, is about 1/3 and 1/2, for the considered 1-D signal and speech signal, respectively. This factor for an image considered is 2/3 and in particular for a textural image it is 1/5.

Theoretical analysis of adaptive harmonic window and its application in frequency extraction of vibration signal

The goal of this paper is to find an excellent adaptive window function for extracting the weak vibration signal and high frequency vibration signal under strong noise.The relationship between windowing transform andfiltering is analyzed first in the paper.The advantage of adjustable time-frequency window of wavelet transform is introduced.Secondly the relationship between harmonic wavelet and multiple analytic band-pass filter is analyzed.The coherence of the multiple analytic band-pass filter and harmonic wavelet base function is discussed,and the characteristic that multiple analytic band-pass filter included in the harmonic wavelet transform is founded.Thirdly,by extending the harmonic wavelet transform,the concept of the adaptive harmonic window and its theoretical equation without decomposition are put forward in this paper.Then comparing with the Hanning window,the good performance of restraining side-lobe leakage possessed by adaptive harmonic window is shown,and the adaptive characteristics of window width changing and analytical center moving of the adaptive harmonic window are presented.Finally,the proposed adaptive harmonic window is applied to weak signal extraction and high frequency orbit extraction of high speed rotor under strong noise,and the satisfactory results are achieved.The application results show that the adaptive harmonic window function can be successfully applied to the actual engineering signal processing.

基于谐波分解恢复弱势信号的高分辨率处理技术

地震资料高分辨率处理是预测薄储集层的有效手段,高分辨率处理的主要目的是有效恢复地震高、低频信息和拓宽频带,同时保持资料的信噪比和保真度。基于谐波分解恢复弱势信号的高分辨率处理技术,以压缩小波变换为基础,根据信号谐波分量对地震高、低频弱势信号进行恢复。首先,基于有效频带内的地震信号,采用压缩小波变换将其分解为各基频信号,然后计算各基频信号的高次谐波与低次谐波,将计算出的高次谐波与低次谐波加入小波变换系数中,最后进行小波逆变换,即可恢复高、低频弱势信号。在实现过程中,仅仅在有效频带内估算基频信号,能较好地保持信号的信噪比。地震信号的小波变换系数与地层反射系数具有一致性,因此该技术具有较高的保真度,可以较好保持相对振幅关系。实际应用表明,在保持资料信噪比的同时,该技术能大幅拓宽地震资料的频带,处理后的地震剖面断点更清楚,分辨能力改善明显,能较好地识别6 000 m以深约40 m的薄储集层。

基于分数阶小波变换的电力系统谐波检测方法

针对稳态和暂态谐波检测易受噪声干扰的问题,提出一种基于分数阶小波变换(fractional wavelet transform,FRWT)的电力系统谐波检测方法。首先,通过快速傅里叶变换(fast Fourier transform,FFT)检测出各频谱的频率,根据频率确定分解层数;其次,利用FRWT对信号进行分解和重构,实现基波与各次谐波分量的分离;最后,对各次谐波分量进行Hilbert变换(Hilbert transform,HT),获取各次谐波分量的频率和幅值检测结果以及暂态扰动的起止时刻。对多种谐波信号的仿真试验结果表明,FRWT方法可以有效实现对含噪信号中稳态和暂态谐波的检测,即使信噪比较低也能保证各项结果的检测精度,证明FRWT方法是检测谐波的一种有效新方法。

基于SVMD-EWT的超声组织谐波成像算法研究

针对超声组织谐波成像中宽带射频回波信号的谐波分离问题,提出了一种基于逐次变分模态分解(SVMD)和经验小波变换(EWT)的信号滤波算法,简称SVMD-EWT。其对信号进行逐次变分模态分解,收集窄带模态的中心频率。结合经验小波变换中自适应频谱曲线局部极小值寻找方法对模态进行分类。将判定为基波成分与谐波成分相互混叠的模态的能量作为优化经验小波变换模态边界的参数,设计经验小波滤波器对超声射频回波信号做滤波处理。仿真和实验表明相比传统的人为给定截止频率的带通滤波器和将发射反相位信号得到的回波信号相加滤波的脉冲反转法,本文提出的方法具有更好的滤波性能和稳定性。带通滤波器和本文方法滤波后生成的乳腺肿瘤谐波B超图对比度分别为15.77 dB和20.78 dB。

小波包分解重构的励磁变压器整流谐波附加损耗计算

谐波损耗是造成励磁变压器安全事故的主要原因之一,损耗值过大易影响正常变电和输电,为此提出基于小波包分解重构的励磁变压器整流谐波附加损耗计算方法。通过获取小波包分解和重构系数,分析励磁变压器两种谐波附加损耗;利用开路和短路实验得到励磁变压器等效电路参数,采用小波包分解重构算法,计算不同谐波次数下的电阻和电抗并与基准值对比,得到励磁变压器整流谐波附加损耗值。最后,选取某种型号的励磁变压器,利用所提方法计算其在不同谐波次数下的附加损耗值,结果表明,得到的损耗值计算结果与实际结果非常接近,验证了所提方法具有较高的实用价值。

海洋平台注水泵机组滚动轴承故障特征提取研究

为了识别海洋平台注水泵机组滚动轴承的故障,文章应用谐波小波包变换,提取了滚动轴承的外圈、内圈及滚动体故障特征信号,分析了滚动轴承振动过大时故障特征信号的变化。结果表明:谐波小波包变换能从强噪声背景中提取微弱的滚动轴承故障特征信号,通过提取的故障特征信号的幅值变化,能判断出滚动轴承的外圈、内圈及滚动体故障。

ICEEMDAN-小波阈值法在谐波信号中的应用

为了在噪声环境下检测电力系统谐波信号,引入一种基于改进自适应噪声集合经验模态分解(ICEEMDAN)与小波阈值降噪的电力系统谐波信号检测新方法。该方法首先通过ICEEMDAN对含噪谐波信号分解,获取若干内在模态分量(IMF),计算每个IMF与原谐波信号的相关系数来确定含噪模态分量;然后使用改进的小波阈值函数对含噪分量进行去噪处理,通过希尔伯特变换(HT)获取谐波信号的幅频信息;最后将去噪后的IMF与优势IMF进行重构得到去噪后的信号。算例分析表明:ICEEMDAN和改进的小波阈值联合检测使得电力系统稳态谐波的平均误差为3.11%,暂态谐波的平均误差为3.64%,信噪比提高且去噪后的信号能够更真实地反映电力系统扰动信号的特征。

基于反向传播-自适应提升算法的谐波阻抗估计

目前,关于量化谐波阻抗的研究大多数是基于系统侧谐波阻抗不发生改变而设定,当系统谐波阻抗变动时,如何估计谐波阻抗的研究相对较少。为此,本文提出一种基于系统谐波阻抗变动背景下的系统谐波阻抗估计新方法。首先,加窗处理谐波电压、电流测量数据,使用二元线性回归法估算系统谐波阻抗,并用小波包变换对测量数据进行分段,以找出系统谐波阻抗变动的时间;其次,采用反向传播-自适应提升算法精确量化每个采样数据段的系统谐波阻抗;最后,通过仿真与实例分析验证本文方法相较于其他方法具有更好的鲁棒性和精确性。

基于小波变换的谐波检测和定位方法研究

电力系统谐波和间谐波的实时精确检测是谐波评估和治理的前提和基础。本文针对瞬态谐波检测和谐波定位问题,将FFT和小波分析方法的效果进行了对比。由分析结果可知,FFT方法能够有效检测出电力系统稳态谐波分量,但是对瞬态谐波分量不敏感,且无法定位谐波发生时间,小波变换方法能够检测出稳态和瞬态谐波分量,并能对谐波起止时间准确定位,但是存在频率混叠缺陷。

基于小波变换的电力系统谐波频率测量算法研究

电力系统的谐波频率测量是保障电力质量与系统稳定运行的关键环节。传统的谐波检测方法,如模拟带通或带阻滤波器,是基于傅立叶变换和瞬时无功功率理论的测量,虽然在特定条件下表现良好,但在实时跟踪与精度要求上仍存在不足。随着计算能力的提升和数学工具的发展,基于小波变换的方法在谐波分析中展现出独特的优势。文章对小波变换在谐波频率测量中的应用进行了深入研究,提出了一种实时跟踪算法,该算法能够有效识别和测量电力系统中的谐波频率。通过仿真实验验证,表明该算法可以在复杂电力环境下可以表现出较高的精度和可靠性。小波变换不仅可以提高谐波检测的准确性,还具备较强的抗干扰能力,为电力系统的谐波分析提供了一种更加有效、实时的解决方案。

基于改进小波阈值去噪和CEEMDAN-HT融合的谐波检测技术

针对电网环境噪声导致自适应噪声的CEEMDAN谐波检测精度低的问题,文中提出了一种基于改进小波阈值去噪和CEEMDAN-HT融合的谐波检测技术。利用修正因子T j自适应调整阈值,通过可调参数τ调节阈值函数软、硬特性,并将改进的小波阈值去噪方法应用于谐波信号的预处理。经预处理的信号再进行CEEMDAN分解,可有效抑制模态混叠束缚。运用相关度判据去除虚假分量,并利用Hilbert变换解调包含谐波特征的分量,准确提取其幅频信息。经MATLAB仿真可知,改进小波阈值去噪与CEEMDAN-HT的融合算法可将稳态谐波检测平均误差被控制在1%以下,暂态谐波检测平均误差被控制在2.1%以下,呈现出良好的抗噪性能。

基于小波变换的分布式光伏并网谐波检测方法

常规的谐波检测方法以目标谐波信号识别为主,增强的谐波信号能量导致源信号损伤,从而出现较大的谐波检测误差,因此设计了基于小波变换的分布式光伏并网谐波检测方法,通过提取分布式光伏并网谐波信号的频谱特征,修正小波函数得到的信号幅值、相位数据。利用小波变换,构建光伏并网谐波检测模型,对谐波信号进行EMD分解,得出更加准确的谐波电流信号,从而实现分布式光伏并网谐波的高效检测。采用仿真实验的方式,验证了该检测方法的检测误差更小,能应用于实际生活中。

基于TDLAS技术与小波变换去噪算法的甲烷浓度检测

为进一步提高甲烷浓度检测精度,搭建了基于TDLAS(tunable diode laser absorption spectroscopy)技术的甲烷浓度检测实验系统,利用甲烷在波长1653.72 nm处吸收强度很高且可以最大限度消除其他气体干扰的特性,通过提取二次谐波信号实现甲烷浓度检测。然后分别采用heursure硬阈值算法、heursure软阈值算法和sqtwolog固定阈值算法作为小波变换阈值算法,通过分析未去噪及小波变换去噪处理后得到的甲烷吸收信号谱图、甲烷二次谐波信号谱图、甲烷吸收信号的信噪比和均方根误差,优选sqtwolog固定阈值算法作为小波变换阈值算法。不同浓度的甲烷标气线性拟合实验及特定浓度的甲烷标气重复性实验结果表明:通过小波变换(采用sqtwolog固定阈值算法)能有效降低噪声干扰,去噪处理后提取的二次谐波信号与甲烷真实浓度拟合优度R^(2)为0.984,拟合效果更佳。采用TDLAS技术结合小波变换去噪算法,实现甲烷浓度检测的同时也能提高甲烷浓度检测精度。

基于多孔分数阶小波变换的谐波检测新方法

基于分数阶小波变换的电力谐波检测方法是一项新兴的研究成果,其可较好地解决新型电力系统中谐波检测受噪声干扰的问题,提高谐波检测精度。然而传统离散分数阶小波变换均基于Mallat算法完成,其实现过程中的下采样操作将影响谐波信号的检测精度。针对这一问题,文章将非下采样多孔算法与分数阶小波变换相结合得到一种改进的离散分数阶小波变换实现方法,并在此基础上提出一种基于多孔分数阶小波变换的谐波检测新方法。此外,文章采用基于分数阶频谱四阶原点矩的方法确定最佳分数阶变换阶次,有效降低计算复杂度。实验结果表明,新方法对稳态谐波、短时谐波及时变谐波均能有效实现信号降噪和分离,并能对信号分量的幅值、频率及定位信息实现高精度检测。

电力系统谐波抑制仿真研究

利用傅里叶转换与FTT转换技术对所建立的谐波模型进行了模拟,发现利用FTT的时频特征可以提取出该信号的时间-频率特征,能够有效地消除干扰,并能有效地探测出信号的频域特征,而傅里叶转换则能有效地反映出信号的频域特征。将傅里叶分析与FTT分析相比较,将其优点相融合,利用小波分析法对信号中的突变点进行了提取和除噪,再利用傅氏转换对各个稳态的频域和振幅进行精确的检测。