基于连续小波变换的三自由度链模型模态参数辨识

采用连续小波变换的方法,在大型结构模态参数辨识方面提出了系统化的辨识流程,并对相近的频率所造成的辨识难点提出了解决的办法。通过三自由度模型模态参数的辨识,表明连续小波变换对于大型结构的模态参数辨识准确度高,具有现实意义。     

机械故障诊断中微弱信号处理特征的提取

论述了门限小波变换的四阶累积量在微弱信号特征提取中的功能、实现和应用条件。比较了连续小波变换(CWT)、短时傅里叶变换(STFT)、维格纳-威利(WVD)利用高阶累积量和没有利用高阶累积量情况下,提取微弱信号特征的特性。仿真表明,连续小波变换的四阶累积量在微弱信号特征提取中有较好的效果     

基于小波方法的时变动力系统参数识别

针对离散时变动力学系统,运用Daubechies小波对其激励和系统响应信号作小波变换,将变换后的响应和激励代入微分方程,利用Daubechies小波尺度函数的正交性,将微分方程转换成简单的代数方程组,求解方程组,从而识别出时变系统的物理参数.推导了单自由度和多自由度线性时变系统的参数识别方程,介绍了小波系数的计算方法,阐述了系统时变参数的识别思路.通过对系统参数连续、周期和突变3种时变情况进行仿真研究,证明了方法的正确性和有效性.     

小波基函数在故障诊断中的最佳选择

通过定性与定量的分析,提出了在对冲击信号进行连续小波变换时选择最佳小波基函数的方法和小波变换 后故障特征提取效果优劣的检验手段,并且得出了对于冲击性信号的连续小波变换,小波基函数的最佳选择为 Morlet函数的结论。     

复解析最优小波解调法

针对齿轮箱故障信号的多分量多频调制特点,结合基于复解析小波变换的信号分量的包络幅值提取原理,提出了一种基于峭度和平滑指数的最优小波解调技术。首先,采用连续小波变换谱的能量熵作为时间尺度分辨率的度量指标,将其应用到Morlet分析小波的参数优化选择中;其次,对常规小波峭度计算法进行改进,结合平滑指数对最优小波变化尺度进行迭代搜索;最后,针对解调谱线繁杂密集无法分辨的问题,采用频谱细化的方法提高了复解析小波解调谱的频谱精度。应用试验结果表明,该复解析最优小波解调方法较传统的解调方法,可以更加准确有效地提出齿轮箱复杂信号中的微弱故障特征。     

基于小波变换的传感器故障诊断仿真研究

主要介绍了小波变换的原理和应用,着重研究了基于连续小波变换极值点的方法在传感器故障诊断中的应用.最后,利用Matlab软件中的Simulink工具箱建立传感器系统的故障模型进行仿真研究,证明该方法的可行性.     

压电柔性结构模态参数辨识的小波变换法

利用小波变换的线性变换特性,结合Morlet小波的性质和结构振动系统自由响应信号的形式,构造了一组小波族,并通过利用2-范数对结构响应信号的小波变换进行归一化以及小波变换局部极大值的计算,获得了结构的模态频率与模态振型.仿真结果表明,Morlet连续小波变换不同尺度之间重叠的冗余性,使得利用尺度与信号频率的变换关系来进行准确的密集模态参数辨识比较困难,而本文方法简单有效,比直接利用Morlet小波更为直观、方便和准确,对压电柔性结构的低频密集模态频率和模态振型能够很好地辨识.     

小波分析在信号处理中的应用研究

小波分析是对傅里叶变换的继承和发展,首先介绍了小波理论产生的实际背景和小波变换的基本原理,特别是多尺度分析理论。最后,通过实验实现了分形信号的多尺度连续小波变换。     

Mallat算法的光学实现方法

现有的光学小波变换方法均基于连续小波变换,基于离散信号的小波变换算法（Mallat算法）的光学小波变换还没有出现,这阻碍了光学小波变换应用的发展。针对这一问题,分析利用光学4f系统实现Mallat算法的基本原理,提出Mallat算法的光学实现方法。针对空间光调制器只能实现非负的实函数,且CCD只能记录光的强度,给出一种应用于光学4f系统的光学小波滤波器的设计方法。使用该种光学小波滤波器,利用光学4f系统实现Mallat算法的小波分解部分,并通过数值计算实现Mallat算法的小波重构部分。仿真分析和光学实验结果验证了方法的正确性。     

基于连续小波变换的汽轮发电机组轴系扭振及其主动控制模拟试验的研究

根据连续小波变换具有较二进离散小波变换和小波包变换更精细的尺度分辨率的特点,提出了适用于工程界的小波变换结果的表述方法,研究了小波分析方法在汽轮发电机组轴系振动及其主动控制模拟试验中的适用性问题。通过对汽轮发电机组轴系振动及其主动控制模拟试验数据的小波分析,得到了较为满意的结论。     

PARAMETERS OPTIMIZATION OF CONTINUOUS WAVELET TRANSFORM AND ITS APPLICATION IN ACOUSTIC EMISSION SIGNAL ANALYSIS OF ROLLING BEARING

Morlet wavelet is suitable to extract the impulse components of mechanical fault signals. And thus its continuous wavelet transform (CWT) has been successfully used in the field of fault diagnosis. The principle of scale selection in CWT is discussed. Based on genetic algorithm,an opti-mization strategy for the waveform parameters of the mother wavelet is proposed with wavelet en-tropy as the optimization target. Based on the optimized waveform parameters,the wavelet scalogram is used to analyze the simulated acoustic emission (AE) signal and real AE signal of rolling bearing. The results indicate that the proposed method is useful and efficient to improve the quality of CWT.     

基于复小波变换相位谱的齿轮故障诊断

提出了一种基于复小波变换诊断齿轮故障的新方法。利用Mexican-hat调制复小波基函数对齿轮振动信号进行连续小波变换，再作相位的频谱分析，可以突出边频带结构，识别不同故障模式。试验数据的分析结果表明，该方法适用于齿轮故障诊断，与传统的自功率谱方法以及基于实值小波的小波变换方法相比，这种方法效果更好。     

遗传小波网络在齿轮箱故障诊断中的应用

针对小波神经网络常用的反向传播算法普遍存在收敛速度慢,容易陷入局部极小点,网络参数的选取只能凭实验和经验来确定等缺点,提出了一种基于遗传算法优化的小波神经网络并应用于齿轮的故障诊断。仿真结果表明,该方法充分的发挥了遗传算法的全局寻优能力,小波分析的非线性逼近能力和神经网络的自学习特性,优化了系统的收敛速度和故障诊断的精度。     

Haar wavelet for machine fault diagnosis

Continuous wavelet transform (CWT) is a kind of time–frequency analysis method commonly used in machine fault diagnosis. Unlike Fourier transform, the wavelet in CWT can be selected flexibly. In engineering application, there is a problem of how to select a suitable wavelet. At present, the selecting method mainly depends on the waveform similarity between the signal required to extract and the wavelet. This method is imperfect. For example, Haar wavelet possesses the rectangular waveform in its supporting field and dissimilarity to any component in the machine signal. It is rarely used in machine diagnosis. However, the time–frequency periodicity of Haar wavelet continuous wavelet transform (HCWT) should be useful in revealing the features in signals. In addition, Haar wavelets under different scales have good low-pass filter characteristic in frequency domain, particularly under larger scales, and that can allow HCWT to detect the lower frequency signal. These merits are presented in this paper and applied to diagnose three types of machine faults. Furthermore, in order to verify the effect of Haar wavelet, the diagnosis information obtained by HCWT is compared with that by Morlet wavelet continuous wavelet transform (MCWT), which is popular in machine diagnosis. The results demonstrate that Haar wavelet is also a feasible wavelet in machine fault diagnosis and HCWT can provide abundant graphic features for diagnosis than MCWT.     

基于自适应最优Morlet小波的滚动轴承故障诊断

滚动轴承早期故障信号中故障信息比较微弱常常被强噪声所掩盖,增加了对滚动轴承故障诊断的难度。针对这一问题,笔者提出了基于自适应最优Morlet小波变换的滚动轴承故障诊断方法。首先,利用粒子群优化算法对Morlet小波变换的核心参数进行自适应寻优,在获得最优Morlet小波的同时保证了良好的带通滤波性能;然后,将最优Morlet小波对滚动轴承早期故障信号进行滤波去噪,提高信号的信噪比;最后,对最优Morlet小波滤波信号进行包络谱分析,通过包络谱中的主导频率成分与滚动轴承各元件的故障特征频率对比从而判断轴承的故障位置。仿真数据和实测数据分析结果证明,笔者所提方法能够有效提取故障信号中的特征信息,具有一定的有效性。     

Adaptive wavelet transform for vibration signal modelling and application in fault diagnosis of water hydraulic motor

There has been an increasing application of water hydraulics in industries due to growing concern on the environmental, health and safety issues. The fault diagnosis of water hydraulic motor is important for improving water hydraulic system reliability and performance. In this paper, fault diagnosis of water hydraulic motor in water hydraulic system is investigated based on adaptive wavelet analysis. A novel method for modelling the vibration signal based on the adaptive wavelet transform (AWT) is proposed. The linear combination of wavelets is introduced as wavelet itself and adapted for the particular vibration signal, which goes beyond adapting parameters of a fixed-shape wavelet. The AWT procedure based on the parametric optimisation by genetic algorithm (GA) is developed. The model-based method by AWT is applied to extract the features in the fault diagnosis of the water hydraulic motor. This technique for de-noising the corrupted simulation signal shows that it can improve the signal-to-noise ratio of the vibration signal. The results of the experimental signal demonstrate the characteristic vibration signal details in fine resolution. The magnitude plots of the continuous wavelet transform (CWT) show the characteristic signal's energy in time and frequency domain which can be used as feature values for fault diagnosis of water hydraulic motor.     

连续小波变换在滚动轴承故障诊断中的应用

采用连续小波分析的方法对滚动轴承振动和速度信号进行处理,提取滚动轴承故障特征。通过对滚轴承在正常、内圈剥落、外圈剥落及滚动体落情况下的振动加速度信号进行分析,验证了这种方法的有效性。     

小波变换在瞬变信号检测中的计算机仿真及应用

利用连续小波变换对模拟瞬变信号进行了仿真 ,试验结果表明 ,此方法灵敏度高 ,对噪声具有较好的鲁棒性。在此基础上探讨了基于离散小波变换的刀具破损的检测情况 ,取得了较好的效果 ,解决了以往此类故障由于干扰大而难以识别的困难 ,为故障诊断领域提供了一种非常有效的手段     

Detection of signal transients based on wavelet and statistics for machine fault diagnosis

This paper presents a transient detection method that combines continuous wavelet transform (CWT) and Kolmogorov–Smirnov (K–S) test for machine fault diagnosis. According to this method, the CWT represents the signal in the time-scale plane, and the proposed “step-by-step detection” based on K–S test identifies the transient coefficients. Simulation study shows that the transient feature can be effectively identified in the time-scale plane with the K–S test. Moreover, the transients can be further transformed back into the time domain through the inverse CWT. The proposed method is then utilized in the gearbox vibration transient detection for fault diagnosis, and the results show that the transient features both expressed in the time-scale plane and re-constructed in the time domain characterize the gearbox condition and fault severity development more clearly than the original time domain signal. The proposed method is also applied to the vibration signals of cone bearings with the localized fault in the inner race, outer race and the rolling elements, respectively. The detected transients indicate not only the existence of the bearing faults, but also the information about the fault severity to a certain degree.     

S变换用于滚动轴承故障信号冲击特征提取

为从低信噪比的滚动轴承故障信号中提取出冲击特征,以便于进行轴承故障诊断,引入S变换的信号处理方法。以短时傅里叶变换（short time Fourier transform,简称STFT）以及连续小波变换（continuous wavelet transform,简称CWT）为理论基础,分别推导得出了连续S变换的定义式,并利用快速傅里叶变换（fast Fourier transform,简称FFT）实现S变换离散化计算。S变换克服了STFT时频分辨率固定的缺点,弥补了CWT缺乏相位信息的不足。仿真信号研究表明,S变换在信号整个频带上具有良好的时频分辨率和时频聚集性,能够提取低信噪比信号中的冲击特征,且性能优于STFT和CWT。最后对一组实际的滚动球轴承故障振动信号进行S变换处理,结果表明,S变换能够方便有效地从中提取出周期性的冲击特征,从而指导滚动轴承相关故障的诊断。