A Quotient Space Approximation Model of Multiresolution Signal Analysis

In this paper, we present a quotient space approximation model of multiresolution signal analysis and discuss the properties and characteristics of the model. Then the comparison between wavelet transform and the quotient space approximation is made. First, when wavelet transform is viewed from the new quotient space approximation perspective, it may help us to gain an insight into the essence of multiresolution signal analysis. Second, from the similarity between wavelet and quotient space approximations, it is possible to transfer the rich wavelet techniques into the latter so that a new way for multiresolution analysis may be found.

Characteristics of acoustic emission signals in damp cracking coal rocks

A uniaxial load experiment on coal rocks at different stress rates was carried out, based on the characteristics of acoustic emission (AE) signals in cracking coal rocks, decomposition, de-noising and reconstruction for the AE signals through wavelet packet transform for solving the current problems created by the presence of noise in AE signals and the existing problems in AE signal processing. The results show that the various characteristics of AE signals in coal rocks cracking under different situations can be clearly reflected, after the AE signals are de-noised by the wavelet packet. Compared to dry coal rocks, the number of AE occurrences in damp coal rocks was significantly reduced, as well as the average amplitude. The number of AE occurrences in damp and dry coal rocks clearly increased with increases in the loading rate, but the largest amplitude of the AE signals in damp coal rocks has been reduced. There is no clear evidence of change in dry coal rocks.

Improved signal de-noising in underwater acoustic noise using S-transform: A performance evaluation and comparison with the wavelet transform

Sound waves propagate well underwater making it useful for target locating and communication.Underwater acoustic noise(UWAN)affects the reliability in applications where the noise comes from multiple sources.In this paper,a novel signal de-noising technique is proposed using S-transform.From the time-frequency representation,de-noising is performed using soft thresholding with universal threshold estimation which is then reconstructed.The UWAN used for the validation is sea truth data collected at Desaru beach on the eastern shore of Johor in Malaysia with the use of broadband hydrophones.The comparison is made with the more conventionally used wavelet transform de-noising method.Two types of signals are evaluated:fixed frequency signals and time-varying signals.The results demonstrate that the proposed method shows better signal to noise ratio(SNR)by 4 dB and lower root mean square error(RMSE)by 3 dB achieved at the Nyquist sampling frequency compared to the previously proposed de-noising method like wavelet transform.

Novel Adaptive Beamforming Algorithm Based on Wavelet Packet Transform

An analysis of the received signal of array antennas shows that the received signal has multi-resolution characteristics, and hence the wavelet packet theory can be used to detect the signal. By emplying wavelet packet theory to adaptive beamforming, a wavelet packet transform-based adaptive beamforming algorithm (WP-ABF) is proposed . This WP-ABF algorithm uses wavelet packet transform as the preprocessing, and the wavelet packet transformed signal uses least mean square algorithm to implement the ~adaptive beamforming. White noise can be wiped off under wavelet packet transform according to the different characteristics of signal and white under the wavelet packet transform. Theoretical analysis and simulations demonstrate that the proposed WP-ABF algorithm converges faster than the conventional adaptive beamforming algorithm and the wavelet transform-based beamforming algorithm. Simulation results also reveal that the convergence of the algorithm relates closely to the wavelet base and series; that is, the algorithm convergence gets better with the increasing of series, and for the same series of wavelet base the convergence gets better with the increasing of regularity.

基于瞬时无功功率理论的电能质量扰动检测、定位与分类方法

提出了一种对电能质量扰动进行检测、定位、辨识与分类的有效方法。首先对含有噪声的信号进行小波去噪处理,得到信噪比较高的信号,再对去噪后的信号进行差变处理得到差变信号,通过小波去噪和信号差变法来确定扰动的位置和持续时间。将去噪后的单相电压信号通过移相?60°得到三相电压,并用三相电压代替三相电流,利用瞬时无功功率理论计算出瞬时有功,进而计算出瞬时电压幅值,根据扰动的持续时间和扰动的幅度对扰动类型进行识别。

基于离散余弦变换和小波变换的电能质量扰动信号检测方法

综合离散余弦变换和小波变换模极大值原理在时频分析中的优点,提出了基于离散余弦变换和小波变换的电能质量扰动信号检测方法。先通过离散余弦变换检测出各种基频干扰(电压暂降、电压暂升和电压间断)和各次谐波(包括暂态谐波),再利用小波变换模极大值原理检测出暂态振荡和暂态脉冲,并实现扰动时间和扰动幅值的测定。该方法具备较强的抗噪能力,仿真结果验证了该方法的有效性。

小波包–能量谱在高压断路器机械故障诊断中的应用

文章提出了一种利用小波包变换对高压断路器振动信号进行处理的新方法,应用小波包–能量谱分析得到各尺度上的能量百分比作为特征参量对高压断路器进行故障诊断。小波包变换能将已知信号按任意时频分辨率分解到更加精细频带,且提高了信号处理的频率分辨率,小波包正交分解后的信号具有各频带信号独立、能量守衡的特点。仿真结果表明,小波包–能量谱用于高压断路器机械故障诊断中是行之有效的。

基于小波变换的新型暂态行波方向保护

以往的行波方向保护只对电压或电流中的一种行波波头进行信号处理,因此失去了另一电气量的信息,有时会引起方向保护的误判。为此作者提出了一种新型线路保护方案,利用小波变换提取线路故障时保护测量点的暂态电压、电流的特征量,并对二者的波形进行比较,根据其相似程度对线路的区内、区外故障作出准确、快速的判断。采用ATP(Alternative Transients Program)对各种类型故障所做的仿真计算验证了所提出的保护原理的正确性和有效性。

基于小波变换的诱发电位信号去噪研究

基于一维小波变换的方法对诱发电位信号进行了去噪研究。采用小波多分辨率分析方法来处理信号的高频———即噪声部分,分别对仿真信号和实际信号进行消噪处理,比较了去噪前后信号的信噪比等性能指标。实验表明,小波变换方法能较好地消除诱发电位信号的噪声。