高斯复小波变换在齿轮故障诊断中的应用

提出了利用具有高斯复包络的小波函数族分析齿轮故障振动信号的方法.利用高斯小波基函数从相位的角度提取齿轮振动信号的故障信息,可突出边频带结构,有效识别故障模式.对仿真信号及试验故障振动信号的分析结果表明,该方法适用于齿轮故障诊断,与传统的自功率谱方法相比,抗噪声干扰能力强.     

HHT和Morlet小波变换在齿轮故障信息提取中的对比研究

文章描述了基于振动信号的Morlet小波变换和HHT(Hilbert-Huang变换)齿轮故障信息提取方法,并分别用来对四类齿轮进行故障信息提取,得到各状态齿轮振动信号的Morlet小波谱和Hilbert谱。实验研究表明:Morlet小波变换和HHT都可用于齿轮故障信息提取,但Hilbert谱分析比Morlet小波谱分析在时间和频率域都有较高的分辨率,且HHT比Morlet小波变换有更高的计算效率,更适用于故障信号微弱、振动信号数据量大的齿轮故障信息提取。     

高斯线调频小波变换与齿轮故障边频带分析

提出了基于高斯线调频小波变换的功率谱齿轮故障诊断方法.利用高斯线调频小波变换对齿轮振动信号作了谱分析,进而对齿轮故障进行分析.对实验数据的分析结果表明:该方法优于经典的自功率谱估计和一般的小波分析方法对齿轮局部故障诊断;该方法可突出齿轮的边频带结构,适用于齿轮的局部故障诊断.     

齿轮箱点蚀的在线故障诊断系统

基于傅里叶变换的谱分析具有一定的局限性,是一种全局变换,而小波具有很好的时频分辨能力,将Hilbert变换和小波分析方法结合起来对齿轮箱点蚀故障进行了研究。以JZQ250型齿轮点蚀故障为分析对象,对测得的振动加速度信号进行小波变换和Hilbert变换。为了更好地进行人机交互,利用VC++的强大界面开发能力把其嵌入,实现对数据的分析,并设计一个规则库。结果表明:所设计的界面可行并可有效诊断故障。     

基于经验模态分解法和Hilbert谱的齿轮箱故障诊断

将经验模态分解法(Empirical Mode Decomposition,简称EMD)和Hilbert谱引入到齿轮箱故障诊断,提出了一种新的齿轮箱故障诊断方法.通过运用该方法和连续小波变换分别对某齿轮箱齿轮齿根裂纹故障振动信号进行分析,结果表明,该方法能更有效地提取齿轮故障信息,提高了齿轮故障诊断的准确性.这种自适应的信号处理方法非常适合分析非线性、非平稳过程.     

基于提升小波变换与EEMD的神经网络齿轮故障诊断方法

针对单一的信号处理诊断方法难以实现齿轮故障准确诊断的局限性,文章将提升小波变换、集成经验模态分解(ensemble empirical mode decomposition,EEMD)与相关系数相结合,提出一种新的信号消噪方法,并在此方法的基础上,分别利用BP、Elman和RBF神经网络完成了齿轮故障诊断。首先采用提升小波变换对故障信号进行初步消噪,然后对其作EEMD分解,得到一组固有模态函数(intrinsic mode function,IMF)分量;然后计算各分量的相关系数,剔除相关性较小的伪分量后进行重构,完成二次消噪;最后计算剩余分量的能量特征,并将其作为神经网络的输入向量,进而完成齿轮断齿、裂纹和磨损状况下的故障诊断。仿真分析和应用实例表明:基于提升小波变换与EEMD分解并结合相关系数筛选的消噪方法,比仅用提升小波方法消噪的效果更好。三种神经网络均成功辨别出了齿轮的故障类型,但不同方法各有优劣之处;就诊断效率和准确性而言,BP神经网络的诊断效果最好。     

小波包与Hilbert分析法在旋转设备故障诊断中的应用

文中分析了对齿轮和滚动轴承的故障诊断在旋转机械设备中的重要性,介绍了小波包分析和Hilbert分析的理论,针对齿轮和滚动轴承故障信号的非平稳性特点,提出了一种基于小波包分析和Hilbert分析法相结合的故障诊断方法.在matlab中应用该方法对齿轮的点蚀故障和滚动轴承的内环故障进行诊断,仿真结果表明,基于小波包分析与Hilbert分析法相结合的方法可以有效地提取齿轮和滚动轴承的故障特征频率,从而可以迅速地识别出齿轮和滚动轴承的故障类型.     

振动监测技术在齿轮裂纹故障诊断中的应用

在对一级齿轮箱的振动信号进行快速傅里叶变换和小波包变换的基础上,提取各个小波包系数的峭度和偏态,并选择分辨率较高的小波包系数的峭度和偏态作为齿轮裂纹的故障特征。最后通过基于粒子群优化算法(Particle swarm optimization, PSO)的支持向量机(Support vector machine, SVM)模型进行齿轮裂纹故障特征分类,其中,PSO主要用来优化SVM模型的核函数的关键参数,避免出现局部最优和过拟合的问题。计算结果表明,和其它算法相比,提出的齿轮裂纹故障诊断方法在分类精度和计算效率方面具有综合优势。     

基于DT-LMD机床轴承故障信号提取研究

为了准确得到机床故障轴承的运行状态,结合双树复小波变换(Daul-Tree Complex Wavelet Transform,DT-CWT)和局域均值方法 (LMD)分解的方法提出了一种新的方法 (DT-LMD),对轴承故障振动信号提取,首先利用双树复小波变换对信号进行降噪和重构,其次通过局域均值方法分解,再次利用该方法对机床轴承实际振动信号进行分解,提取其能量特征值并将特征值进行归一化处理,得到各个分量的能量值;最后判断轴承的故障类型。     

基于Autogram的齿轮断齿故障特征提取方法

针对复杂生产背景下产生的强噪声淹没齿轮有效故障特征信息的问题,利用Autogram方法对其进行特征提取。该方法利用最大重叠离散小波包变换,对齿轮断齿故障振动信号进行不同层数分解处理,每层得到若干个信号,被称为“node”。为了更加全面地描述故障特征信息,对每个node进行包络谱的3种无偏自相关谱峭度求取,以便选取合适node作为信号源进行下一步分析。最后,对该信号源引入阈值处理,以便加强频谱分析的全面性,实现对齿轮断齿故障特征信息的有效提取。通过对比分析仿真和实测齿轮故障振动信号,验证了该方法的有效性。     

基于小波包变换预处理的铝电解槽针振信息元分析与诊断

简述了小波包变换的基本原理及利用小波包对电压信号进行分解的方法。针对铝电解槽电压波动信号的频谱特点。采用小波包分析方法提取了电压信号的特征向量。将信号分解到8个频段内。进行预处理得到频段能量特征向量。应用BP神经网络建立了特征向量到振针信息元之间的映射。仿真结果表明，小波包分析能够有效地将隐藏在正常电压信号之中的早期弱故障信号提取出来，从而发现槽子的早期不良症状。     

Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines

A fault signal diagnosis technique for internal combustion engines that uses a continuous wavelet transform algorithm is presented in this paper. The use of mechanical vibration and acoustic emission signals for fault diagnosis in rotating machinery has grown significantly due to advances in the progress of digital signal processing algorithms and implementation techniques. The conventional diagnosis technology using acoustic and vibration signals already exists in the form of techniques applying the time and frequency domain of signals, and analyzing the difference of signals in the spectrum. Unfortunately, in some applications the performance is limited, such as when a smearing problem arises at various rates of engine revolution, or when the signals caused by a damaged element are buried in broadband background noise. In the present study, a continuous wavelet transform technique for the fault signal diagnosis is proposed. In the experimental work, the proposed continuous wavelet algorithm was used for fault signal diagnosis in an internal combustion engine and its cooling system. The experimental results indicated that the proposed continuous wavelet transform technique is effective in fault signal diagnosis for both experimental cases. Furthermore, a characteristic analysis and experimental comparison of the vibration signal and acoustic emission signal analysis with the proposed algorithm are also presented in this report.     

基于小波包能量分布特征的齿轮故障诊断方法研究

齿轮的裂纹故障不仅影响机械系统的整体性能,还会导致机器损坏,因此,研究了齿轮裂纹长度的故障诊断方法。以多传感振动信号为研究对象,将小波包各个频段的能量比系数作为齿轮裂纹的故障特征,并通过改进的神经网络模型进行特征分类,实现齿轮裂纹长度的故障诊断。研究结果表明:所提出的故障诊断方法识别率高(97.5%),通用性好,能有效辨识不同工况下的齿轮故障。     

Evaluation of pile defects using complex continuous wavelet transform analysis

In recent years, the technique of wavelet transform has been applied widely in signal processing in different fields, including non-destructive testing of pile foundations. However, it was used mostly in signal filtering and the analysis of time-frequency diagram. This paper successfully utilized complex continuous wavelet transform to determine pile length and locations of defects on pile foundations by analyzing the time-frequency-phase angle diagram in different frequency band. Six piles with different types of defects were installed and tested to verify the proposed approach in this study. The results shows that complex continuous wavelet transform not only is able to provide high resolution results in different frequency bands, which is similar to that of continuous wavelet transform, but also simplifies the identification of the reflection of defects using 3D phase spectrogram. The location of defects can then be easily determined using phase diagram under the corresponding specific frequency.     

Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition

Due to the importance of rolling bearings as the most widely used machine elements, it is necessary to establish a suitable condition monitoring procedure to prevent malfunctions and breakages during operation. This paper presents a new method for detecting localized bearing defects based on wavelet transform. Bearing race faults have been detected by using discrete wavelet transform (DWT). Vibration signals from ball bearings having single and multiple point defects on inner race, outer race, ball fault and combination of these faults have been considered for analysis. Wavelet transform provides a variable resolution time–frequency distribution from which periodic structural ringing due to repetitive force impulses, generated upon the passing of each rolling element over the defect, are detected. It is found that the impulses appear periodically with a time period corresponding to characteristic defect frequencies. In this study, the diagnoses of ball bearing race faults have been investigated using wavelet transform. These results are compared with feature extraction data and results from spectrum analysis. It has been clearly shown that DWT can be used as an effective tool for detecting single and multiple faults in ball bearings. This paper also presents a new method of pattern recognition for bearing fault monitoring using hidden Markov Models (HMMs). Experimental results show that successful bearing fault detection rates as high as 99% can be achieved by this approach.     

Evaluation of the dispersive phase and group velocities using harmonic wavelet transform

The evaluation of the dispersive phase and group velocities having sensible physical meaning is of practical importance in various NDT techniques. In this paper, we propose a method based on the harmonic wavelet transform to evaluate dispersive phase and group velocities. To apply the harmonic wavelet transform in the evaluation of dispersive velocity, the meaning of the harmonic wavelet coefficient is interpreted from a different point of view. Based on these concepts, the step-by-step procedure to evaluate the dispersive phase and group velocities was proposed. In the proposed method, both phase and group velocities can be obtained directly from information extracted from data based on the harmonic wavelet transform. To evaluate the validity of the proposed method, numerical simulations of the multi-layered system were performed and the phase and group velocities determined by the proposed method match very well with theoretical velocities showing the good potential of the proposed method.     

Application of the wavelet transform and the enhanced Fourier spectrum in the impact echo test

The objective of this study is to develop a reliable and effective method to analyze the signal of the impact echo test. The impact echo test is a nondestructive testing technique for civil structures. In the test, the surface response of the target structure due to an impact is measured. Then, the Fourier transform is adopted to transform the signal from the time domain to the frequency domain. Owing to the multiple reflections induced by cracks, voids, or other interfaces, peaks will form in the Fourier spectrum. The frequencies of the peaks can then be used to determine the size of the structure or the location of the defect.

Several difficulties are encountered when applying the Fourier transform to impact echo data. Because the impact echo data are non-stationary and contains multiple reflections, ripples and multiple peaks appear in the Fourier spectrum, which may mislead the follow-up diagnosis. Furthermore, the existence of the high-energy surface wave and structural vibrations often complicates the spectrum and makes the data interpretation even more difficult.

To overcome these difficulties, this research adopts the wavelet transform in the analysis of impact echo data. Theoretically, the wavelet transform can avoid ripple and multiple-peak phenomena. Furthermore, the frequency range and time span of surface wave can be observed in the wavelet scalogram. However, the spectral resolution of the wavelet marginal spectrum is inferior to that of the Fourier transform. Therefore, two approaches are proposed in this paper. One is to combine the Fourier spectrum and the wavelet marginal spectrum to determine the precise location of the echo peak. The other is to take the product of the two spectra to establish the enhanced Fourier spectrum. As such, the interference in the Fourier spectrum is suppressed while the peak is enhanced. Numerical and experimental tests were performed to verify the effectiveness and reliability of the proposed approaches.     

变转速变载荷工况下的齿轮智能故障诊断北大核心

针对变转速变载荷工况下的齿轮故障检测、识别和分类问题,提出一种基于最大重叠离散小波包变换和人工神经网络的智能故障诊断新方法。研究自相关谱峭度图中的最大重叠离散小波包变换,并采用它将复杂的齿轮故障振动信号分解为频带和称为节点的中心频率。推导出每个节点的平方包络的自相关,以便计算每个节点在每个分解层次上的峭度,减少了非周期性脉冲和噪声的影响。将上一步得到的特征矩阵作为径向基函数神经网络的输入,从而实现齿轮状态的自动分类。并在变转速变载荷(16种)工况下对健康状态和5种不同类型齿轮故障的齿轮箱进行了具体测试分析。结果表明:该方法可以更好地提取特征信息,为齿轮故障诊断定位合适的解调频带,提高了所有工况下齿轮故障诊断的准确率。     

滚动轴承微弱故障信号检测的研究

研究了信号局部奇异性在小波变换下的特性.根据故障信号和噪声的局部奇异性在小波变换下模极大值在不同尺度上的传播特性不同的特点,利用小波分解重构算法,对滚动轴承振动信号进行了分解、去噪、重构和谱分析.实验表明,小波减噪方法非常适于滚动轴承微弱故障信号的检测.