基于FFT和WPT的电能质量数据压缩算法研究

针对电力系统智能化及远程化日益提升的大量电能质量数据传输、储存问题,提出基于快速傅里叶变换(FFT)和小波包变换(WPT)相结合的有损压缩算法,并采用LZW处理数据,通过Matlab的仿真平台构建了4种常规的稳态扰动信号与5种常见的暂态信号模型,在强噪声环境下去噪、检测和压缩电压电漉信号.实例结果表明,该方法验证了压缩算法的有效性与优越性,可供参考.     

改进小波包联合PNN的风电故障诊断研究

针对风电机组故障信号的非平稳性以及故障与征兆的非线性映射导致的故障识别困难问题,提出了改进型的节点重构小波包频带能量谱与PNN(概率神经网络)的联合故障诊断新方法。文章深入分析了传统小波包频带错乱的问题,借助傅里叶变换与傅里叶逆变换改进了小波包,消除了小波包频带错乱的缺陷。首次采用改进型小波包提取故障信号特征量作为PNN的输入,然后利用PNN快速准确的非线性映射能力进行故障诊断。最后,采用风力发电机故障试验台的故障轴承的实际数据对所提方法进行验证,结果表明,所提方法可行且有效。     

基于独立分量分析及小波变换的内燃机辐射噪声盲源分离和识别

为了分离和识别内燃机噪声源,结合独立分量分析和小波变换技术对内燃机辐射噪声信号进行了盲源分离和声源识别的研究.根据独立分量分析的基本原理,采用基于负熵极大的FastICA算法对4缸柴油机的辐射噪声信号进行了盲源分离,将噪声信号分解成一系列独立分量.采用快速傅里叶变换和小波变换技术对各个独立分量进行了分析,结合时频分析的结果和内燃机各噪声源信号的频谱结构,确定了分离得到的各独立分量与内燃机不同噪声源的对应关系.研究结果表明:这些独立分量分别对应着柴油机的燃烧噪声、活塞敲击噪声、正时齿轮噪声及排气辐射噪声等噪声源.     

基于第二代小波变换的振动信号去噪与故障诊断

旋转机械故障振动信号存在不同形式的波形特征,传统小波去噪中,小波分解的结果与所采用的小波基函数有关,选用不适当的小波基函数会冲淡振动信号的局部特征信息,而造成原始信号的部分有用的细节信息丢失。为了克服上述缺陷,提出一种基于第二代小波变换的振动信号预处理方法,即针对分析信号的局部特征,以预测方差最小为目标,对每个样本选择最佳的预测算子,使小波基函数始终能够匹配信号的局部特征。仿真试验表明,该方法克服了传统小波去噪中降噪信号丢失了部分细节信息的缺点,不仅可有效地去除故障诊断振动信号的噪声,而且能够保留信号的局部信息。     

基于频谱包络改进的EWT方法的滚动轴承故障诊断研究

经验小波变换方法被证明是一种有效的滚动轴承故障诊断方法,但该方法的分析精度依赖于频谱的合理分割。因此,文章提出了一种基于改进经验小波变换的故障特征提取方法。首先,通过连接若干个信号频谱的局部极大值来获取频谱包络线;然后,设定阈值,消除噪声干扰;最后,根据频谱包络线的局部极小值来自适应地确定频谱分割边界。工程实例分析表明,基于改进经验小波变换的故障特征提取方法,提高了对故障特征频带的分离精度,在滚动轴承故障特征提取方面表现出一定的优越性。     

压气机喘振声音信号的快速傅里叶变换和小波变换分析

对压气机的喘振声音信号进行了试验,并进一步利用快速傅里叶变换对试验数据进行了频谱分析,分析结果表明压气机喘振的声音信号频率在50Hz以下。为了摆脱压气机运行噪声对喘振信号的干扰,利用Daubechies(dbN)小波系中的db8系的小波对测定数据进行处理,得到了可以表征压气机进入喘振时声音的特征信号,为实际生产中使用声音信号监测压气机状态以及故障诊断提供了良好的理论基础和依据。     

基于SR-WT的输电线路双端故障定位方法

电力系统中行波在传输过程中往往伴随着大量的高频噪声,导致进行故障测距时信号波头不易识别,从而产生测距误差。对此,提出一种基于随机共振和小波变换(SR-WT)的故障测距方法,即先通过随机共振处理含噪信号,将噪声的能量转移到行波信号上,从而实现初步减弱噪声信号提高行波信号的功能;再通过小波变换进一步分解得出所需行波信号波头信息,进而通过双端法进行故障测距。仿真试验结果表明,该方法能够在强噪声干扰下准确提取出所需的行波信号信息,有利于提高测距精度。     

汽轮机转子典型振动故障诊断模型训练系统

文中基于支持向量机分类理论,运用二叉树算法建立了汽轮机转子典型故障的多分类诊断模型训练系统。通过小波包分析、经验模态分解(EMD)和傅里叶变换(FFT)三种信号处理方法训练出的诊断模型训练系统对测试样本分类的正确率、比较三种训练方法的优劣。     

小波神经网络法在柴油机故障诊断中的应用

用小波分析作信号处理手段提取柴油机振声信号特征量 ,以神经网络作为故障模式识别手段 ,进行了柴油机故障的振声诊断方法研究。针对柴油机振声信号的非平稳时变特性 ,应用小波理论中的小波包方法对其进行处理 ,结果表明小波分析是比傅里叶分析更为有效的处理柴油机振声这类非平稳信号的方法。在此基础上 ,研究了用神经网络实现根据小波包分解结果识别柴油机故障状态的方法。     

新型光纤温度测量系统信号的仿真研究

介绍了用小波阈值滤波对光纤温度测量系统信号进行仿真的方法。该方法基于信号和白噪声在小波变换下具有不同的特性,将含噪信号进行多尺度小波分解,采用软闽值方法将其高频部分进行量化处理,再进行重构。实验证明该方法有效去除了信号中的噪声,方便地用软件实现了比色测温信号的处理,提高了系统的测温精度。     

基于小波包IRN网络的燃气轮机转子故障诊断

提出一种基于小波包和带有偏差单元的内部回归神经网络相结合的燃气轮机转子故障诊断方法。利用小波包分析去除噪声信号干扰,简化燃机转子故障特征提取。带有偏差单元的内部回归神经网络的记忆特性好,收敛速度快、稳定性强。小波包和带有偏差单元的内部回归神经网络的结合,大大提高了诊断速度及诊断准确性。     

基于小波分析的柴油机故障信号特征的提取

本文提出了一种新的柴油机表面振动信号的故障特征的提取方法，利用柴油机表面振动信号经过小波降噪处理，有效地剔除柴油机表面振动信号的噪声干扰，提高信号的信噪比。用小波包提取降噪后振动信号的能量特征参数。以表征柴油机故障特征，建立起能量到柴油机故障的映射关系。实际研究表明这一特征提取方法是有效的。     

基于小波包变换的柴油机燃油喷射系统故障诊断

本文在介绍了小波包变换理论后，利用小波包变换对柴油机燃油喷射系统的常见故障进行了分析，并和傅立叶变换进行了比较，证明了前者比后者能提取更多的关于柴油机工作状态的特征信息。     

基于小波包和Markov链的低压电力线背景噪声重构

针对传统方法难以重构出时域特性和频域特性与真实低压电力线背景噪声一致的背景噪声问题,搭建了噪声测量平台实测了背景噪声,提出了一种基于小波包变换与Markov链相结合的背景噪声重构方法,通过小波包变换得到真实背景噪声在不同频带中的小波包分解系数,并利用Markov链对分解系数进行统计,模拟生成与实测噪声分解系数统计规律相同的仿真分解系数,将仿真分解系数重构后即可得到背景噪声。实例仿真结果表明,该方法重构的背景噪声在时域波形上与实测噪声极为相似,且功率密度谱变化趋势也与实测噪声基本一致,可作为电力线载波通信设备性能测试的可靠噪声源。     

基于小波包的泵站机组振动信号特征分析

提出了一种应用小波包分析对泵站机组振动信号进行特征分析的方法。与小波分析相比,小波包分析能对信号的高频频带进一步分解,提高了频率分辨率。利用小波包对泵站机组振动信号进行了信号压缩与消噪以及奇异性分析,为诊断机组振动故障提供了决策依据。对泵站机组主轴摆度和轴承振动实测信号进行了分析,结果表明小波包分析可有效提取原始信号的特征。     

Noise analysis of digital ultrasonic nondestructive evaluation system

Being one of the five most commonly used nondestructive testing (NDT) routines, ultrasonic testing (UT) is under fast development in recent years, with more attention being focused on quantitative testing and nondestructive evaluation (NDE). In the evaluation of pressure vessels and piping, UT is utilized not only in manufacturing quality controlling, but also in-service monitoring and residual life prediction, such as the inspection of welded joints, monitoring of crack propagation, evaluation of materials property deterioration.In ultrasonic NDE and quantitative NDT, one of the main factors disturbing the reliability and accuracy of test is noise encountered during inspection. At present, a digitized instrument is increasingly preferred in practice. Considering every step, from wave emitting to digitalization of analog signal, the following noises usually emerge—the electronic circuit noise of instrument; structure noise caused by grain boundaries of material under testing; ringing noise due to oscillation of probe; digital noise of finite-word digital system; pulse noise by virtue of fluctuation of inspection circumstance. Among these, the most serious is the structure noise encountered in the testing of coarse-grained austenite stainless steel, which affects the defect signal, making the least detectable defect size increase. In the present paper the characteristics, detriment and elimination algorithms of electrical noise, pulse noise, ringing noise and structure noise in a digital ultrasonic NDE system are discussed. A physical model of the digital ultrasonic NDE system is established, and noises are classified into different categories from the viewpoint of the model. The characteristics of electrical noise are analyzed; an algorithm of extremum filtering constructed to eliminate the pulse noise; high-pass filter and wavelet packet are employed to process ringing noise; the features of structure noise are studied and it is de-noised by wavelet transform (WT) and wavelet packet transform (WPT). The results obtained from real-world signal show that the electrical noise can be taken approximately as white noise with a Gaussian distribution. The algorithm of extremum filtering can filter the pulse noise without any effect on other information in the signal. Wavelet packet algorithm is more suitable for the elimination of long-term ringing noise than high-pass filter under the condition of less loss of defect echo. Processing the signals of coarse-grained austenite stainless steel samples with defects by use of WT and WPT concludes that as structure noise can be divided into certain frequency bands, generally different from those of defect echoes by WT and WPT, the defect can be pointed out, and the signal-to-noise ratio enhanced substantially after the threshold processing of frequency components of signals followed reconstruction.     

Bearing monitoring in the wind turbine drivetrain: A comparative study of the FFT and wavelet transforms

Wind turbines are often plagued by premature component failures, with drivetrain bearings being particularly subjected to these failures. To identify failing components, vibration condition monitoring has emerged and grown substantially. The fast Fourier transform (FFT) is the major signal processing method of vibrations. Recently, the wavelet transforms have been used more frequently in bearing vibration research, with one alternative being the discrete wavelet transform (DWT). Here, the low‐frequency component of the signal is repeatedly decomposed into approximative and detailed coefficients using a predefined mother wavelet. An extension to this is the wavelet packet transform (WPT), which decomposes the entire frequency domain and stores the wavelet coefficients in packets. How wavelet transforms and FFT compare regarding fault detection in wind turbine drivetrain bearings has been largely overlooked in literature when applied on field data, with non‐ideal placement of sensors and uncertain parameters influencing the measurements. This study consists of a comprehensive comparison of the FFT, a three‐level DWT, and the WPT when applied on enveloped vibration measurements from two 2.5‐MW wind turbine gearbox bearing failures. The frequency content is compared by calculating a robust condition indicator by summation of the harmonics and shaft speed sidebands of the bearing fault frequencies. Results show a higher performance of the WPT when used as a field vibration measurement analysis tool compared with the FFT as it detects one bearing failure earlier and more clearly, leading to a more stable alarm setting and avoidable, costly false alarms.     

小波包能量谱在内燃机噪声信号故障诊断中的应用

基于噪声信号的故障诊断方法具有易于实现离机、无损检测的优越性,将噪声信号故障诊断方法引入内燃机气门间隙故障诊断中。阐述了小波包能量谱提取的基本原理和方法,通过对内燃机在不同转速不同状态下运行时的噪声信号小波包能量谱的提取研究,确定了噪声信号能量波动范围。并在对比分析中发现,利用内燃机噪声信号在特征频带内的能量波动特征研究方法,可以有效识别气门间隙故障。     

A new noise-controlled second-order enhanced stochastic resonance method with its application in wind turbine drivetrain fault diagnosis

Condition monitoring of a wind turbine is important to extend the wind turbine system's reliability and useful life. However, in many cases, to extract feature components becomes challenging and the applicability of information drops down due to the large amount of noise. Stochastic resonance (SR), used as a method of utilising noise to amplify weak signals in nonlinear systems, can detect weak signals overwhelmed in the noise. Therefore, a new noise-controlled second-order enhanced SR method based on the Morlet wavelet transform is proposed to extract fault feature for wind turbine vibration signals in the present study. The second-order SR method can obtain better denoising effect and higher signal-to-noise ratio (SNR) of resonance output by means of twice integral transform compared with the traditional SR method. Morlet wavelet transform can obtain finer frequency partitions and overcome the frequency aliasing compared with the classical wavelet transform. Therefore, through Morlet wavelet transform, the noise intensity of different scales can be adjusted to realize the resonance detection of weak periodic signal whatever it is a low-frequency signal or high-frequency signal. Thus the method is well-suited for enhancement of weak fault identification, whose effectiveness has been verified by the practical vibration signals carrying fault information. Finally, the proposed method has been applied to extract feature of the looseness fault of shaft coupling of wind turbine successfully.