Current signature analysis of induction motor mechanical faults by wavelet packet decomposition

This paper presents a novel approach to induction motor current signature analysis based on wavelet packet decomposition (WPD) of the stator current. The novelty of the proposed method lies in the fact that by using WPD method the inherent nonstationary nature of stator current can be accurately considered. The key characteristics of the proposed method are its ability to provide feature representations of multiple frequency resolutions for faulty modes, ability to clearly differentiate between healthy and faulty conditions, and its applicability to nonstationary signals. Successful implementation of the system for two types of faults, i.e., rotor bar breakage and air-gap eccentricity is demonstrated here. The results are validated based on both simulation and experiments of a 5-hp induction motor.     

DSSS系统中自适应小波包干扰抵消器

本文提出了一种DSSS系统中基于小波包变换域的自适应干扰抵消算法（AWPT）,分析了小波包变抵抵消窄带干扰和脉冲干扰的机理,提出了优化准则和相应的自适应抵消算法,通过计算机仿真研究了其抗窄带和脉冲干扰的能力,并与传统的加窗DFT干扰抵消和子带变换干扰抵消作了比较。     

VBR视频流量的小波包分解及其长时预测

长时预测是VBR视频流量预测领域中的难点问题.针对其时变、非线性以及长相关性等特点,提出一种多尺度分解的VBR视频业务的特征提取方法.选择具有任意多分辨分解特性的小波包,对其进行空间划分并求解适合视频信号特征提取的最优分解基.基于最优基对视频信号进行快速多尺度分解,得到了各级节点的小波系数矩阵,建立了基于最小二乘支持向量机与最小均方的小波系数预测方法.最后,根据预测小波系数,进一步提出了基于小波系数逆变换的视频流量长时预测方法.仿真结果验证了此算法的有效性.     

基于小波包变换及TRLMS抑制脉冲干扰的方法

为了解决航空无线电导航服务频段附近的脉冲干扰影响接收机工作的问题,并进一步提高全球导航卫星系统(GNSS)中的脉冲干扰抑制能力,提出了基于小波包变换及变换域最小均方根算法(TRLMS)来抑制脉冲干扰。通过最佳小波函数分解,跟踪定位脉冲干扰所处频带,并采用TRLMS自适应滤波技术取代常规小波包固定阈值方法对干扰进行抑制。仿真实验结果表明,所提方法相比其他的干扰抑制均方根误差更小,解决了有用信号丢失的问题,具有更好的干扰抑制性能。     

基于小波包分解的舰船辐射噪声特征提取方法研究

由于舰船辐射噪声有其自身的非高斯、非线性和非白噪声的非平稳特性,传统时域或频域的信号处理方法不能满足对这类信号的处理.而小波包变换对信号的非平稳特性有适应性.本文通过利用小波包变换分解舰船的辐射噪声,统计噪声在各个频段上的能量分布,将统计的分频段能量作为舰船的目标特征,实现舰船目分类识别标的目的.结合Matlab对舰船辐射噪声进行仿真,提取的目标一和目标二的特征,实现了舰船的分类识别的目的,验证的方法的有效性.     

基于小波包分解的船舶发电机轴承故障检测

为了实现对船舶发电机轴承进行故障检测,通过对三相定子电流的Park失量模信号进行小波包分解,求相应子频带小波包分解的均方根值(RMS)作为表征电机轴承的故障特征,以此作为发电机轴承故障诊断的依据。在实验室条件下,选择一款发电机并人为设置轴承故障进行试验验证,实验结果表明,提出的方法能够有效的识别发电机的轴承故障。     

Statistical modelling of the wavelet coefficients with different bases and decomposition levels

Various wavelet coefficient statistics are useful to increase the compression efficiency of images. The distribution of the wavelet coefficients within a sub-band affects how the decompression values should be adjusted. A generalised Gaussian distribution model is seen to be applicable from a theoretical point of view, and a convenient way is provided to estimate the parameters from the empirical data. This knowledge is applied to examine the best statistical modelling of the wavelet coefficients for different wavelet bases and decomposition levels.     

短期电力负荷在小波包分解下的径向基神经网络预测方法

针对短期电力负荷预测问题,提出一种在小波包分解下的径向基神经网络预测方法。通过小波包分析,将电力载荷及其温度变量对称地分解为低频的近似系数和高频的细节系数。针对不同的小波系数,设计径向基神经网络作为预测器,并通过试错法确定网络合适的结构。网络的训练过程中,采用滑动窗口数据选择策略减少数据样本集,采用随机梯度法更新权值、中心位置和扩展参数。预测的小波系数用于重构出最终的电力载荷值。与前馈多层神经网络的对比数值,实验结果表明,新提出的方法具有较高的预测准确性。     

Study on feature extraction method in border monitoring system using optimum wavelet packet decomposition

Border monitoring plays a key role in the national defense. In this study, we applied the sound identification technology on the border monitoring, and assumed an ideal border monitoring sound target recognition system. Feature extraction is a crucial step in this recognition system. As the border sounds are of non-stationary signals, the traditional methods failed to extract such kind of features. Fortunately, wavelet packet transform (WPT) can provide an arbitrary time-frequency decomposition for the signals. Based on WPT, a novel feature extraction method using optimum wavelet packet decomposition (OWPD) was proposed. According to the characteristics analysis of the border monitoring sounds using WPT, the signals were analyzed by selective multi-scale wavelet packet decomposition (i.e. OWPD), and then we built the meaningful and compact energy feature vectors as the input vectors of the BP neural network, in order to recognize the border monitoring sound. Extensive experimental results showed that this feature extraction method has convincing recognition efficiency.     

基于Bark尺度小波包和Teager能量算子的语音去噪方法

针对小波包分解的高频混叠和频带顺序混乱,采取了一种改进小波包算法,采用一种更精细的Bark尺度小波包对带噪语音进行分解,以模拟人耳的听觉特性.对分解系数计算Teager能量算子(TEO),并使用一种简单的随TEO变化而调节的自适应阈值门限对带噪语音进行阈值去噪,不需要判别清浊音和无声段,实验证明,该方法在多种噪声下都能取得较好语音增强效果,特别是在有色噪声和非平稳噪声下.对于较低信噪比的语音也能获得一定改善.     

A software package for the decomposition of long-term multichannel EMG signals using wavelet coefficients

This paper presents a method to decompose multichannel long-term intramuscular electromyogram (EMG) signals. In contrast to existing decomposition methods which only support short registration periods or single-channel recordings of signals of constant muscle effort, the decomposition software EMG-LODEC (ElectroMyoGram LOng-term DEComposition) is especially designed for multichannel long-term recordings of signals of slight muscle movements. A wavelet-based, hierarchical cluster analysis algorithm estimates the number of classes [motor units (MUs)], distinguishes single MUAPs from superpositions, and sets up the shape of the template for each class. Using three channels and a weighted averaging method to track action potential (AP) shape changes improve the analysis. In the last step, nonclassified segments, i.e., segments containing superimposed APs, are decomposed into their units using class-mean signals. Based on experiments on simulated and long-term recorded EMG signals, our software is capable of providing reliable decompositions with satisfying accuracy. EMG-LODEC is suitable for the study of MU discharge patterns and recruitment order in healthy subjects and patients during long-term measurements.     

Cancellation performance of co-site antenna interference cancellation system for AM interference

The interference cancellation performance of actual co-site antenna interference cancellation system (CICS) under amplitude modulation jamming signal was analyzed.The expressions of the steady-state weights and theirs structure characteristics of the interference cancellation system under the AM signal were given.The formulae of the interference cancellation ratio (ICR) for carrier frequency and side frequency were derived.The influence factors and the influence rules of the ICR were analyzed.The steady state weights of the interference cancellation system (ICS) were the weighting of the carrier frequency optimal weights and the edge frequency optimal weights according to the power ratio.The increase of the signal bandwidth will increase the discreteness of the carrier frequency optimal weights and the edge frequency optimal weights and lead to the decrease of the interference cancellation ratio (ICR).The decrease degree of the edge frequency ICR was greater than that of the carrier frequency ICR.To improve ICR by increasing the gain was limited.The relationship between the interference cancellation ratio limit and the equivalent transmission delay was given.The design method of the interference cancellation system to improve the ICR under the AM signal was proposed.Finally,the correctness and validity of the theoretical analysis was verified by simulation.     

自适应干扰抵消系统的双边带调幅干扰抵消特性

根据系统模型给出了自适应干扰抵消系统针对双边带调幅制干扰时的稳态权值表达式,推导出干扰抵消比计算式,分析了影响干扰抵消效果的因素及其作用规律,并给出了提高干扰抵消比的设计思路和方法。对于双边带调幅制信号,通过提高系统增益来提高干扰抵消比,将受到信号带宽和等效传输延迟距离的制约。信号带宽越宽或等效传输延迟距离越大,通过增大增益所能实现的干扰抵消比提高越有限。对于一定的信号带宽,可以通过增益和等效传输延迟距离的联合设计来提高干扰抵消比。仿真结果验证了理论分析的正确性和可行性。     

Wavelet and wavelet packet compression of electrocardiograms

Wavelets and wavelet packets have recently emerged as powerful tools for signal compression. Wavelet and wavelet packet-based compression algorithms based on embedded zerotree wavelet (EZW) coding are developed for electrocardiogram (ECG) signals, and eight different wavelets are evaluated for their ability to compress Holter ECG data. Pilot data from a blind evaluation of compressed ECG's by cardiologists suggest that the clinically useful information present in original ECG signals is preserved by 8:1 compression, and in most cases 16:1 compressed ECG's are clinically useful     

Wavelet and wavelet packet compression of phonocardiograms

The first reported method intended for the compression of the phonocardiogram, using wavelet coefficients thresholding, together with Huffman and run length encoding coding techniques to obtain further compression is described. Results show better compression rates and lower distortion than standard audio compression techniques.     

针对小波域量化隐藏方法的图像监测技术研究

基于对图像小波变化后各子带系数的不同统计特性，提出了一种针对小波域信息隐藏算法的新型图像隐写术分析技术。通过对原始图像和隐藏信息后的图像的小波变换系数子带直方图的分析，得出了小波域信息隐藏对子带系数的改变特性。进一步对系数进行傅立叶变换，从而量化信息隐藏对系数的影响程度。实验结果表明该方法适用于小波域量化隐藏信息算法的检测。     

小波包分解对心理声学模型的改进

为了改变常用心理声学模型中均匀谱分析造成的时频分辨率不足的问题,采用小波包分解对信号进行分析.通过Matlab对信号进行小波包分解处理,代替常用心理声学模型的FFT,改善了时频分辨率不足的问题,且通过小波包分解得到的频带划分.比常用心理声学模型得到的频带划分更接近于人耳的临界频带,更适应于人耳的听觉特性.     

An efficient method of P2P traffic identification based on wavelet packet decomposition and kernel principal component analysis

Peer‐to‐peer (P2P) traffic identification is currently an important challenge to network management and measurement. Many approaches based on statistics have been proposed to identify P2P traffic. However, flow features extracted by traditional methods are rough and one‐sided, which might lead to inaccuracy identification of network traffic. Besides, P2P traffic has too many statistical features, which is a challenge to the time complexity and space complexity of the classifier. This work focuses on the study of flow features. First, micro features of flow signals are extracted based on wavelet packet decomposition, and we combine them with the traditional features into combination features. The experimental results show that combination features have better performance than traditional features for P2P traffic identification, and 16 kinds of wavelet functions were tested to find the best one. Second, a feature reduction algorithm based on improved kernel principal component analysis is provided. The results show that the feature reduction algorithm proposed in this paper plays good performance to P2P traffic identification, because it could greatly reduced the number of features while having no affection on identification accuracy. Copyright © 2012 John Wiley & Sons, Ltd.     

Hybrid signal decomposition based on instantaneous harmonic parameters and perceptually motivated wavelet packets for scalable audio coding

The paper presents a complete framework for hybrid representation of audio and speech signals that can be used in coding applications. The parameterization approach is based on the three-part model (sinusoids, transients and noise). The essential contributions of the paper can be summarized as follows: (i) a precise mathematical solution to the problem of instantaneous harmonic parameters estimation that can be applied to nonstationary (amplitude and frequency modulated) signals. The instantaneous harmonic parameters (magnitude, frequency and phase) are calculated as the result of the narrow-band filtering of signals. The frequency-modulated filters synthesis with the closed form impulse response has been proposed. The filter frequency bounds can be determined during the components frequency tracking and can be adjusted according to the fundamental frequency modulations; (ii) a practical technique of instantaneous harmonic analysis and numerical evaluation of its performance; (iii) a new transient parameterization scheme based on matching pursuit with frame-based psychoacoustic optimized wavelet packet dictionary. The choice of most relevant coefficients is based on maximizing the matching between the auditory excitation scalograms of original and modeled signals; (iv) the given hybrid analysis system is applied to speech and audio signals in order to validate the proposed methods.     

An Improved Cancellation of Nonlinear Intersymbolinter ference for Voiceband Data Transmission

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