基于硬态切削力信号刀具切削状态的分析

阐述了小波分析及小波去噪的基本理论 ,针对付氏变换和短时付氏变换的缺点 ,提出了基于小波变换的切削力信号分析方法。小波多分辨分析能够实现PCBN刀具的切削力信号的任意精度与尺度的时频域分解 ,从而判断PCBN刀具的切削状态 ,根据切削力信号的不同特征及时调整切削参数 ,减少刀具破损的发生 ,并能够揭示典型切削力信号的分布特征     

小波包时频分析及其特性

在两种典型的非平稳信号分析方法--小波包变换与短时傅里叶变换的基础上,综合两种方法的优点,提出了小波包时频方法.建立了相应的小波包时频分量谱、小波包时频分量幅度谱、小波包时频谱、小波包时频幅度谱等概念.证明了小波包时频分析的能量守恒性,形成了一套较完善的分析体系.算例表明,该分析方法在诊断奇异、检测信号深层次细节等方面具有一些独特性质.     

小波分析在汽车发动机机械故障诊断中的应用

文中阐述了汽车发动机机械故障诊断的理论方法,讨论了小波变换的分析方法.由于小波变换具有传统频谱分析方法所没有的时一频分析特征,特别适合于非平稳信号的分析与处理,应用该方法对实测信号进行了有效的时频分析.     

虚拟式多功能时频分析系统的研究与开发

介绍了典型的时频分析方法及所研发的虚拟式多功能时频分析系统。该仪器系统功能十分强大,主要包括:零相位数字滤波、短时付氏变换、多分辨时频分析、魏格纳分布、乔伊威廉斯分布、小波和小波包阈值滤波、二进小波及小波包正交分解、连续小波变换等。文中逐一介绍各主要功能的基本原理。给出了仪器的开发、设计、仿真及其应用实例。值得一提的是,在分析极大极小尺度共存信号时,作者所提出的多分辨时频分析方法比其它经典时频分析方法有明显的优势。     

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。     

小波分析及其在机械故障诊断中的应用

简要介绍了傅立叶变换、加窗傅立叶变换以及小波变换的特点,研究了它们在机械故障诊断领域的应用,通过对比可以发现,小波变换具有很强的时频局部化功能,可以有效应用于非平稳信号的分析,弥补了传统的、基于傅立叶变换的频谱分析方法的缺陷,是一种理想的机械故障诊断的信号处理方法。     

基于线调频小波变换的旋转机械故障诊断

小波变换在设备故障信号处理中得到广泛地应用,然而,小波变换只能消除白色噪声,对有色噪声不起作用.线调频小波变换统一了短时Fourier变换和小波变换的时频分析,是信号的时间-频率-尺度变换,能根据信号的特点自适应生成新的时频窗口.它不仅具有小波变换良好的时频局部性特点,而且它的时频窗口比小波变换的时频窗口更加灵活.本文应用线调频小波变换对旋转机械故障信号进行消噪,效果明显.     

基于S变换的电力系统间谐波检测

电网中常存在许多频率与基波成非整数倍的间谐波.S变换是由连续小波变换和短时傅立叶变换结合发展起来的另一种时频分析方法,具有概念清晰、结果直观等优点.本文首先分析了应用S变换时频分析进行电力系统间谐波检测的基本原理,然后对平稳间谐波和电压波动引起的动态间谐波等典型算例进行了计算仿真.仿真结果表明,该方法能很好地检测平稳间谐波的幅度及频率,并能较好地检测动态变化间谐波,实现间谐波的有效分析.     

高精度时频分析方法研究

时频分析在非平稳信号处理中占有重要的位置,它的主要任务是表述信号的频率成分随时间变化的规律。本文在时频分析的基础上,重点研究了一种新的高精度时频分析方法-希尔伯特黄变换(HHT),并将改进HHT算法与典型时频分析方法(短时傅立叶变换、WD)进行比较,阐述了HHT的应用及优点。     

车内低频加速噪声信号的时频分析方法比较研究

汽车加速噪声是典型的非平稳噪声,对其进行信号特征提取比较困难。采用短时傅里叶变换(STFT)、小波变换(WT)、平滑伪维格纳-威尔分布(SPWVD)以及希尔伯特黄变换(HHT)等四种时频分析方法,对加速工况下的车内低频噪声信号特征进行了分析比较。结果表明:采用STFT、SPWVD和WT得到的噪声信号能量时频分布基本一致,仅在细节上有所差异;而HHT是根据信号自身特点对信号进行自适应分解,在高信噪比条件下能给出时频特征的准确表达,可作为非平稳车辆噪声特征提取的有效方法。     

Vibration analysis of rotating machinery using time-frequency analysis and wavelet techniques

Time-frequency analysis, including the wavelet transform, is one of the new and powerful tools in the important field of structural health monitoring, using vibration analysis. Commonly-used signal analysis techniques, based on spectral approaches such as the fast Fourier transform, are powerful in diagnosing a variety of vibration-related problems in rotating machinery. Although these techniques provide powerful diagnostic tools in stationary conditions, they fail to do so in several practical cases involving non-stationary data, which could result either from fast operational conditions, such as the fast start-up of an electrical motor, or from the presence of a fault causing a discontinuity in the vibration signal being monitored. Although the short-time Fourier transform compensates well for the loss of time information incurred by the fast Fourier transform, it fails to successfully resolve fast-changing signals (such as transient signals) resulting from non-stationary environments. To mitigate this situation, wavelet transform tools are considered in this paper as they are superior to both the fast and short-time Fourier transforms in effectively analyzing non-stationary signals. These wavelet tools are applied here, with a suitable choice of a mother wavelet function, to a vibration monitoring system to accurately detect and localize faults occurring in this system. Two cases producing non-stationary signals are considered: stator-to-blade rubbing, and fast start-up and coast-down of a rotor. Two powerful wavelet techniques, namely the continuous wavelet and wavelet packet transforms, are used for the analysis of the monitored vibration signals. In addition, a novel algorithm is proposed and implemented here, which combines these two techniques and the idea of windowing a signal into a number of shaft revolutions to localize faults.     

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.     

基于TVAR的自适应时频分析及在故障诊断中的应用

研究了非平稳信号的时变自回归（TVAR）建模方法，通过引入基函数将非平稳时变参数的辨识转化为线性时不变问题的辨识；在此基础上，应用带遗忘因子的递归最小二乘算法进行参数估计，实现了信号的自适应时频分析。通过仿真算例将该法与短时Fourier变换、Wigner分布的结果相比较，验证了该方法时频分辨率高的优越性。最后，将该方法应用于轴承的故障诊断，结果表明，该方法用于故障诊断的特征提取是有效的。     

Hilbert-Huang transformation-based time-frequency analysis methods in biomedical signal applications

Hilbert-Huang transformation, wavelet transformation, and Fourier transformation are the principal time-frequency analysis methods. These transformations can be used to discuss the frequency characteristics of linear and stationary signals, the time-frequency features of linear and non-stationary signals, the time-frequency features of non-linear and non-stationary signals, respectively. The Hilbert-Huang transformation is a combination of empirical mode decomposition and Hilbert spectral analysis. The empirical mode decomposition uses the characteristics of signals to adaptively decompose them to several intrinsic mode functions. Hilbert transforms are then used to transform the intrinsic mode functions into instantaneous frequencies, to obtain the signal's time-frequency-energy distributions and features. Hilbert-Huang transformation-based time-frequency analysis can be applied to natural physical signals such as earthquake waves, winds, ocean acoustic signals, mechanical diagnosis signals, and biomedical signals. In previous studies, we examined Hilbert-Huang transformation-based time-frequency analysis of the electroencephalogram FPI signals of clinical alcoholics, and 'sharp I' wave-based Hilbert-Huang transformation time-frequency features. In this paper, we discuss the application of Hilbert-Huang transformation-based time-frequency analysis to biomedical signals, such as electroencephalogram, electrocardiogram signals, electrogastrogram recordings, and speech signals.     

小波基TVAR建模时频分析及在故障诊断中的应用

研究了非平稳信号的时变自回归建模方法,提出了应用小波基函数将非平稳时变参数的辨识转化为线性时不变问题的辨识,在此基础上,应用带遗忘因子的递归最小二乘算法进行参数估计,实现了信号的自适应时频分析。通过仿真算例将该法与短时傅里叶变换、Wigner分布的结果相比较,验证了该方法时频分辨率高的优越性。最后,将该方法应用于轴承的故障诊断,结果表明,该方法用于故障诊断的特征提取是有效的。     

基于参数自适应时频分布的瞬时频率估计

为了跟踪信号在不同时刻的频率变化情况，需要估计其瞬时频率。本文分析了瞬时频率与非平稳信号的时频分布之间的关系，提出了一种采用自适应信号子空间分解的参数自适应时频分布（PAD），以及基于PAD峰值检测的瞬时频率估计方法。数值仿真和对实测信号的瞬时频率估计实验结果表明，该方法对于调频类信号的估计性能优于其他常用的瞬时频率估计法，且抗噪声干扰能力强，为时变频率非平稳信号的瞬时频率估计提供了新的手段。     

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。     

Non-stationary signal analysis based on general parameterized time–frequency transform and its application in the feature extraction of a rotary machine

With the development of large rotary machines for faster and more integrated performance, the condition monitoring and fault diagnosis for them are becoming more challenging. Since the time-frequency (TF) pattern of the vibration signal from the rotary machine often contains condition information and fault feature, the methods based on TF analysis have been widely-used to solve these two problems in the industrial community. This article introduces an effective non-stationary signal analysis method based on the general parameterized time–frequency transform (GPTFT). The GPTFT is achieved by inserting a rotation operator and a shift operator in the short-time Fourier transform. This method can produce a high-concentrated TF pattern with a general kernel. A multi-component instantaneous frequency (IF) extraction method is proposed based on it. The estimation for the IF of every component is accomplished by defining a spectrum concentration index (SCI). Moreover, such an IF estimation process is iteratively operated until all the components are extracted. The tests on three simulation examples and a real vibration signal demonstrate the effectiveness and superiority of our method.     

Hilbert-Huang Transform (HHT) transient analysis of composite panel undergoing high-velocity impact

Fast Fourier transform (FFT) has been widely used to analyze distribution patterns of frequency components in dynamic response signals. Given a stationary dynamic response signal, a fixed frequency distribution pattern can be obtained efficiently using FFT. If the system of concern is not stationary, however, the frequency distribution pattern varies with time, and the variation in that pattern cannot be effectively determined via FFT. To overcome this weakness, time-frequency dual-domain signal analysis methods such as wavelet transform and Hilbert-Huang transform (HHT) have been introduced. HHT has been shown to be particularly effective in analysis of non-stationary signals obtained from non-linear as well as linear systems. In the present study, the transient characteristics of a composite panel undergoing high-velocity impact were investigated. The composite panel, along with the colliding bullet, were modeled using the finite element method. To verify the reliability of the analysis model, an impact experiment was carried out, which proved that the model provides reliable, similar-to-experimental results.