瞬变信息提取与机器诊断

机器的二次信号往往因机器故障产生大量的冲击、摩擦以及运行转速的不稳定、负荷的变化导致非平稳信号的产生.对非平稳信号分析,付氏变换效果不佳,需要研究这类信号的局部时频特征,提取瞬变信息方能准确地诊断.本文介绍处理非平稳信号的新型工具——小波分析、短时付氏变换两种时频分析方法.最后用小波分析、短时付氏变换和付氏变换对机器的实测振动信号进行分析.说明了小波分析、短时付氏变换作为时频分析方法对处理非平稳信号比付氏变换优越.     

基于S变换的热连轧机耦合振动特征提取

提出一种基于短时傅里叶变换的自适应频域滤波方法,将噪声信号与振动特征成功地分离。根据短时傅里叶变换和功率法设定的阀值,自动捕捉了振动信号在不同时间段的优势频率。对振动信号、压下液压缸压力信号和伺服阀给定信号做短时傅里叶变换后,热连轧机振动被诊断为液机耦合振动。利用离散小波变换和S变换相结合的方法对轧机振动信号进行分析,确定轧机起振的时间为液压压下系统的投入时间,证明了热连轧机存在液机耦合振动现象。     

非平稳振动信号分析中Hilbert-Huang变换的对比研究

Hilbert-Huang变换是一种信号分析新方法,特别适合于对非平稳信号进行分析。介绍该方法的基本理论,并利用它对一个典型的旋转机械非平稳振动信号进行分析。然后通过与利用短时傅里叶变换和小波变换所得到的分析结果的对比,研究Hilbert—Huang变换在分析一般非平稳振动信号中的优势和缺陷。最后结合实际应用中遇到的问题,简要论述Hilbert—Huang变换中的经验模态分解在分析频率成分非常靠近的复杂信号时的不足和原因。研究结果表明,Hilbert—Huang变换和其他方法相比,具有分辨能力强、自适应分解、物理意义清晰、信息完整、形式简洁和易于精确分析等优点;同时也存在具有端点效应、实时性稍差和难以将复杂信号中特别靠近的频率成分分解为独立的本征模分量的缺陷。     

机电系统振动信号分析技术的研究

阐述了振动烈度分析和非平稳信号的时频分析。以振动烈度的计算为基础，分析了预警、报警限的设定方法；最后论述了非平稳信号分析法中的短时傅立叶变换和小波变换，为不同分析法的选用提供了参考。     

基于现代信号处理方法的动态测量误差分析

动态测量误差信号是包含多分量的非平稳信号,成分的复杂性决定了对动态测量误差的分析和处理需要结合多种信号处理方法.如傅里叶变换、小波变换和神经网络等.分析了各种信号处理方法的原理及其在非平稳信号处理中的应用,建立了一个动态测量误差分析仿真系统,对系统输出的动态测量误差进行分析,将总误差进行分解,找出引起此总误差的各组成单元带来的单项误差,为进一步更精确的进行动态精度评定、动态误差诊断与控制,提供了科学的理论依据.     

小波-分形-多 ART2神经网络在汽车发动机故障识别中的应用

利用小波(包)变换和分形理论对汽车发动机的非平稳振动信号进行特征提取,由自组织主成分分析作特征降维,然后用一种新的多ART2神经网络对发动机故障状态进行分类识别,获得了满意的效果。     

Application of an improved kurtogram method for fault diagnosis of rolling element bearings

Kurtogram, due to the superiority of detecting and characterizing transients in a signal, has been proved to be a very powerful and practical tool in machinery fault diagnosis. Kurtogram, based on the short time Fourier transform (STFT) or FIR filters, however, limits the accuracy improvement of kurtogram in extracting transient characteristics from a noisy signal and identifying machinery fault. Therefore, more precise filters need to be developed and incorporated into the kurtogram method to overcome its shortcomings and to further enhance its accuracy in discovering characteristics and detecting faults. The filter based on wavelet packet transform (WPT) can filter out noise and precisely match the fault characteristics of noisy signals. By introducing WPT into kurtogram, this paper proposes an improved kurtogram method adopting WPT as the filter of kurtogram to overcome the shortcomings of the original kurtogram. The vibration signals collected from rolling element bearings are used to demonstrate the improved performance of the proposed method compared with the original kurtogram. The results verify the effectiveness of the method in extracting fault characteristics and diagnosing faults of rolling element bearings.     

基于改进经验小波变换的行星齿轮箱故障诊断

行星齿轮箱振动信号具有复杂多分量和调幅-调频的特点。幅值解调和频率解调方法能够避免传统Fourier频谱中的复杂边带分析,有效识别故障特征频率。经验小波变换通过对信号Fourier频谱的分割构造一组正交滤波器组,能提取具有紧支撑Fourier频谱的单分量成分,再对单分量成分运用Hilbert变换即可实现信号的解调分析。经验小波变换能够有效分离出调幅-调频成分,不存在模态混叠现象,具有完备的理论基础,自适应性好、算法简单、计算速度快。将改进的经验小波变换应用于行星齿轮箱振动信号的解调分析;提出了一种单分量个数的估算方法,解决了经验小波变换中的Fourier频谱划分问题;给出了对故障敏感的信号分量的选取方法,提高了分析的针对性。将改进方法应用于行星齿轮箱振动仿真信号和实验信号分析,验证了该方法的有效性。     

6205-2RS轴承内圈故障特征时频分析

分析6205-2RS轴承内圈故障时变信号,提取故障特征。通过对短时傅里叶变换与Wigner-Ville分布数值仿真实验比较,明确了短时傅里叶变换与Wigner-Ville分布的时频分析优缺点。针对非平稳轴承振动信号,利用短时傅里叶变换,结合Wigner-Ville分布进行了故障特征提取。通过提高短时傅里叶变换汉明窗点数,结合Wigner-Ville分布参数调整与轴承部件的旋转频率计算,给出了6205-2RS轴承内圈故障特征结果。该方法能较准确地诊断轴承内圈的故障现象。     

Fast Fourier and discrete wavelet transforms applied to sensorless vector control induction motor for rotor bar faults diagnosis

This paper presents new techniques to evaluate faults in case of broken rotor bars of induction motors. Procedures are applied with closed-loop control. Electrical and mechanical variables are treated using fast Fourier transform (FFT), and discrete wavelet transform (DWT) at start-up and steady state. The wavelet transform has proven to be an excellent mathematical tool for the detection of the faults particularly broken rotor bars type. As a performance, DWT can provide a local representation of the non-stationary current signals for the healthy machine and with fault. For sensorless control, a Luenberger observer is applied; the estimation rotor speed is analyzed; the effect of the faults in the speed pulsation is compensated; a quadratic current appears and used for fault detection.     

基于LabVIEW的双馈风力发电机组在线谐波分析研究

针对双馈风力发电机组在并网运行时,其电力电子变流装置将向电网注入大量谐波电流的问题,将基于LabVIEW的在线谐波分析技术应用到双馈风力发电机组的谐波监测分析中。开展了针对双馈风力发电机组注入电网谐波的傅里叶分析和小波包分析,提出了将傅里叶变换与小波包变换联合使用的方法;在LabVIEW上搭建了仿真平台,对双馈风力发电机组可能存在的谐波进行了仿真分析试验。试验及研究结果表明,对于平稳信号与非平稳信号进行谐波分析时,将傅里叶变换与小波包变换联合使用,两者的效能得到明显的提高,优于单独使用傅里叶变换或小波包变换,且可更准确地获取基波和谐波的实时波形与频域特性,谐波定位更准确,为谐波补偿提供了更可靠、准确的信息。     

小波降噪技术在柴油机故障诊断中的应用

在柴油机的故障诊断中,对其振动信号的分析是柴油机故障诊断的重要手段,但是由于噪声的干扰而使诊断的精度大打折扣,而传统的傅里叶低通滤波降噪方法对于柴油机这样的非平衡复杂振动信号不能起到好的降噪作用。本文采用小波降噪技术,将柴油机缸盖振动信号进行小波多尺度分解,然后分别进行阈值处理,最后进行小波重构,剔除柴油机的噪声信号,提高信号的信噪比,获得了比较理想的效果。     

Wheel-bearing fault diagnosis of trains using empirical wavelet transform

Rolling bearings are used widely as wheel bearing in trains. Fault detection of the wheel-bearing is of great significance to maintain the safety and comfort of train. Vibration signal analysis is the most popular technique that is used for rolling element bearing monitoring, however, the application of vibration signal analysis for wheel bearings is quite limited in practice. In this paper, a novel method called empirical wavelet transform (EWT) is used for the vibration signal analysis and fault diagnosis of wheel-bearing. The EWT method combines the classic wavelet with the empirical mode decomposition, which is suitable for the non-stationary vibration signals. The effectiveness of the method is validated using both simulated signals and the real wheel-bearing vibration signals. The results show that the EWT provides a good performance in the detection of outer race fault, roller fault, and the compound fault of outer race and roller.     

Isolation and identification of dry bearing faults in induction machine using wavelet transform

Any vibration signal obtained from electromechanical systems contains a level of random changes. These random changes in the measured signal may be due to the random vibrations that can be related to the health of the machine for some faults such as dry bearing fault or bearing ageing. The presence of dry bearing fault, which is caused by the lack of lubricant, increases the level of random vibrations as compared to those obtained in healthy bearing machine. If these random vibrations could be isolated from the measured signal, useful information about bearing health may be obtained. Therefore, in this paper, signals (three line to line voltages, three currents, two vibration signals, four temperatures and one speed signal) obtained from the monitoring system are treated and analyzed using wavelet transform to correlate it to the dry bearing faults in induction machine. In this study, on-line analysis of the acquired signals has been performed using C⁺⁺, while MATLAB has been used to perform the off-line analysis.     

基于小波分析的裂纹梁的损伤识别

阐述了一种基于小波变换的含裂纹梁的损伤识别方法,利用含裂纹梁的一阶模态阵型作为小波分析的力学特征信号,识别损伤的位置和大小.利用小波分析系数的模极大值随分析尺度的传播定位损伤的位置,计算针对于损伤频率信号的能量判断损伤的大小.与以前的小波分析方法相比,此方法确定损伤位置的可靠性高,能识别微小的损伤.利用能量守恒定理和小波分析频段细化的能力,裂纹的定量分辨率高.     

HELICOPTER TRANSMISSION FAULT DETECTION VIA TIME-FREQUENCY,SCALE AND SPECTRAL METHODS

The vast majority of the powerful and effective algorithms in signal processing start with the assumption of stationarity. In addition, the deterministic portion of the signal is often assumed to be composed of complex exponentials which are the solutions to linear time-invariant (LTI) differential equations. Many signals do not comply with these assumptions, however, resulting in disappointment when conventional techniques are used. We now have at hand time–frequency (t–f) and scale transform analyses which can provide new insights into the nature of non-stationary signals. This paper describes some results using reduced interference distributions (RIDs) and scale transforms in the analysis of signals obtained from accelerometers placed strategically on a Westland helicopter transmission. Fault detection algorithms for several types of faults were compared and the methods based on the scale transform performed best followed by RID results. More conventional spectral-analysis-based methods were the least effective.     

A fast multi-level decomposition algorithm for n-dimensional discrete Gabor transforms

Recently, joint spatial and spatial-frequency representations have been used in signal processing of non-stationary signals due to their natural local property and high joint resolution in both the spatial and spatial-frequency domain. However, a major obstacle to their implementation is their large computation requirements. This paper presents a fast n-dimensional Gabor transform and signal reconstruction algorithm employing multi-level parallel decomposition and fast Fourier transform techniques. The algorithm structure lends itself to implementation using VLSI/ASIC technology. Examples of two-dimensional Gabor transform and reconstruction performed on a AT computer demonstrate the substantial computational saving that can be achieved using the fast Gabor transform.     

旋转机械升降速信号的瞬时频率估计

旋转机械的升降速过程是一种非平稳过程,对其测试信号进行分析需要用时频分析方法,如短时傅里叶变换(STFT)及小波变换等方法。对于多分量信号,峰值搜索法经常被用来获取旋转机械在升降速过程中瞬时频率随时间变化的规律。但是,由于噪声和信号中邻近成分间的干扰,直接寻找的结果不能保证其精度和准确性。采用隐马尔可夫模型(Hidden markov models,HMM)进行去噪处理,极大地降低了噪声和干扰对峰值搜索结果的影响,明显提高了结果的精度。仿真试验表明该方法可以取得好的结果。     

Study on denoising filter of underwater vehicle using DWT

In the step processing a digitalized signal,noises are generated by internal or external causes of the system.In order to eliminate these noises,various methods are researched.Among these noise elimination methods,Fourier fast transform （FFT） and short-time Fourier transform （STFT） are widely used.Because they are expressed as a fixed time-frequency domain,they have the disadvantage that the time information about the signal is unknown.In order to overcome these limitations,by using the wavelet transform that provides a variety of time-frequency resolution,multi-resolution analysis can be analysed and a varying noise depending on the time characteristics can be removed more efficiently.Therefore,in this paper,a denoising method of underwater vehicle using discrete wavelet transform （DWT） is proposed.     

RESEARCH OF WAVELET TRANSFORM INSTRUMENT SYSTEM FOR SIGNAL ANALYSIS

0INTRODUCTIONSincethecoddleofthe1970's,abatchOfcompanieslikeHPinU.S.A.havedevelopedvariousdynamicsignalanalyzersbasedonFrsandhavebeenusedinavarietyofsignalmeasurementsandanalysissuchasstmcturalmodelanalysis,statemonitoringandfaultsdiagnosisofeqUipmentandnoiserealtimeoctaveanalysisetc,thisresultsinbigprogressofdynamicsignalanalysis.However,theFFTdefinedbyRiemafmintegTationisonlyabletoanalyzestaystatistical(smooth)signalsbutinefficienttonon-staystatistical(non-smooth)signalswhicharealso…