双谱估计结合EEMD的齿轮故障特征提取

以实测齿轮箱振动信号为分析对象,对锥齿轮系统进行故障特征提取。通过总体平均经验模态分解(EEMD)将采集到的振动信号进行分解,对比分析原始信号功率谱密度特性和各本征模态函数(IMF)频谱特性,抽取相关频带的IMF分量进行信号重构;对重构信号利用直接法进行双谱估计,计算重构信号的双谱熵和非高斯性强度并分析其随试验时间的变化趋势。结果表明,双谱熵和非高斯性强度可以有效反映齿轮运行实时状况,可作为故障诊断和趋势预测的故障特征值。     

小波包-双谱分析和Hilbert-双谱分析的滚动轴承故障诊断方法对比研究

滚动轴承故障诊断是机械故障检测中的一个重要方面. 为了提取滚动轴承微弱振动信号,给出了两种方法小波包-双谱分析法和Hilbert-双谱分析法,并就不同状况对两者进行了对比研究,结果表明,两者都克服了传统谱分析和普通双谱分析中不能充分体现故障信号的缺点.在高斯和非高斯噪声干扰很小时,前者优于后者;在高斯噪声干扰下,而前者更优于后者,在非高斯噪声干扰下,后者则无能为力,前者能够充分体现滚动轴承故障信息.所以小波包-双谱分析法为滚动轴承故障诊断提供了一种准确有效的方法.     

基于双谱分析的滚动轴承故障模式识别

当滚动轴承发生故障时,其产生的振动信号一般是包含较强噪声的非高斯和非线性信号。本文对高阶统计量方法用于滚动轴承故障特征提取进行了研究,提出了基于双谱估计的滚动轴承故障诊断方法。利用这种方法可以同时获得包含滚动轴承故障信号幅值和相位信息的双谱特征图谱。研究表明,双谱图谱可以有效地监测滚动轴承工作状态的模式,因而可以快速地识别滚动轴承不同的故障特征。     

AR双谱在减压阀故障诊断中的应用

双谱可以描述系统的非线性耦合,抑制高斯噪声和保留相位信息。当减压阀系统受到零均值的非高斯噪声干扰时,利用减压阀输出的位移信号建立AR时间序列模型,通过AR双谱进行减压阀故障诊断。结果表明在相同的液压压力下,减压阀在正常工作和出现故障时的双谱存在明显差异,双谱表现出了不同的特征;对于同一实验组,在不同的液压压力下减压阀的双谱特征有略微变化,液压压力对减压阀的故障诊断影响不大,这样有助于在实际应用中对减压阀做出快速诊断。理论和实验说明了双谱分析是实现液压元件故障检测的有效方法之一。     

双谱分析及其在滚动轴承故障诊断中的应用

双谱是处理非线性、非高斯性信号的有力工具,它包含了高阶谱的所有特性.针对滚动轴承具有非线性和非高斯的特性,利用双谱分析方法研究了不同故障模式下滚动轴承的双谱特性以及同一故障类型在不同程度时的双谱特性.实验结果表明,利用双谱特性能很好地区分滚动轴承的不同故障模式以及故障的严重程度,双谱分析方法在滚动轴承故障诊断中具有良好的工程应用前景.     

基于故障诊断的双谱优良特性体现

双谱切片与AR功率谱相比,能有效地去除高斯噪声,保留了信号的相位信息。减压阀振动正常信号和故障信号的AR功率谱与双谱切片均呈现了不同的波峰特性。利用小波包对两种信号的功率谱与双谱切片分别进行了特征提取,并输入BP神经网络以诊断减压阀的故障信号,对两者的诊断效果进行了对比分析,以实验的形式清晰地显示了双谱和功率谱性能上的差异。     

循环平稳相关熵轴承故障诊断方法

相关熵是一种基于信息理论学习和核函数的相似性度量方法，不仅能有效刻画信号的时间和统计特征，而且包含了信号的高阶统计量，因而，相关熵是处理非高斯、非线性信号的有效方法。将相关熵与循环平稳信号处理方法结合，提出了一种基于循环平稳相关熵的轴承故障诊断方法。首先，简述了相关熵的基本概念，推导了循环平稳相关熵函数和循环平稳相关熵谱密度函数公式；其次，分析了循环平稳相关熵轴承故障诊断流程；最后，将循环平稳相关熵应用于轴承内圈、外圈局部裂纹故障振动信号的分析与处理。实验结果表明：相关熵能有效提取轴承故障振动信号中的周期成分，循环平稳相关熵函数和循环平稳相关熵谱密度函数能有效刻画轴承故障的频谱特征，便于进行故障特征提取与识别，验证了提出方法的优越性。     

基于双谱能量算子的碰摩转子故障特征提取

双谱分析能够有效地抑制信号中的高斯噪声,准确地分析信号中存在的二次相位耦合成分.但是,传统的双谱分析方法对于转子全周碰摩故障,尤其是早期碰摩故障,存在丢失信息的问题,无法区分正常转子与碰摩转子,诊断能力较弱.为此,基于双通道矢量谱的概念,提出了双谱能量法,并应用于碰摩转子故障的特征提取.实验结果表明,基于双谱能量的碰摩故障特征提取方法继承了双谱的优良特性,能够更加全面、准确地判别早期碰摩故障,是处理碰摩转子非线性信息的一种有效方法.     

相关熵的降噪机理及其在轴承故障诊断中的应用

针对传统相关函数和谱相关密度难以有效处理强非高斯噪声干扰的问题,提出了一种基于循环平稳相关熵的故障诊断方法。以理论分析和几何图解等方式系统分析了相关熵的降噪机理,以余弦信号和仿真调幅信号为例解释了相关熵以及循环平稳相关熵的降噪机理并验证了其良好的噪声抑制能力;应用循环平稳相关熵方法对轴承内、外圈局部裂纹故障振动信号进行了分析和处理,试验结果表明,循环平稳相关熵谱密度具有解调功能,能准确刻画轴承局部裂纹故障的频谱特征,可有效提取淹没在强噪声环境中的微弱信号。     

Application of time–frequency entropy method based on Hilbert–Huang transform to gear fault diagnosis

When faults occur in the gear, energy distribution of gear vibration signals measured in time–frequency plane would be different from the distribution under the normal state. Therefore, it is possible to detect a fault by comparing the energy distribution of gear vibration signals with and without fault conditions. Hilbert–Huang transform can offer a complete and accurate energy–frequency–time distribution. On the other hand, Shannon entropy could give a useful criterion for analyzing and comparing probability distribution and offer a measure of the information of any distribution. Targeting the feature of energy distribution of gear vibration signal, the merit of entropy and Hilbert–Huang transform, the concept of time–frequency entropy based on Hilbert–Huang transform is defined and furthermore gear fault diagnosis method based on time–frequency entropy is proposed. The analysis results from simulated signals and experimental signals with normal and defective gears show that the diagnosis approach proposed could identify gear status-with or without fault accurately and effectively. However, further study is needed to the classify gear fault pattern such as crack fault or broken teeth.     

Using bispectral distribution as a feature for rotating machinery fault diagnosis

The vibration signals of rotating machinery present a strongly non-linear and non-Gaussian behavior, and bispectrum is well suitable to analyze this kind of signals. Due to modulation or smearing, it is hard to extract the accurate frequency-based features from the bispectrum. A bispectral distribution for machinery fault diagnosis is developed in this paper. The binary images extracted from the bispectra are taken as features to construct the target templates, then, the nearest template classifier is constructed to achieve pattern recognition and fault diagnosis. The computing speed of this method is very high because the proposed algorithm just calculates the number of “1”. Finally, roller bearing and gear fault diagnosis are performed as examples, respectively, to verify the feasibility of the proposed method.     

基于谱熵的齿轮故障诊断方法研究

齿轮故障诊断中,采用何种有效的方法对随机动态信号进行分析和特征提取是关键所在。在实际工程当中所采集到的系统信号不可避免地受到噪声的污染,所以普通的一些处理方法如功率谱分析法等,对噪声的存在很敏感,检测分析结果往往不很理想,且很难准确区分故障。谱熵方法从统计学理论入手,反映了信号的无序性,对噪声具有一定的鲁棒性。本文将谱熵理论引入到机械齿轮传动系统中,对齿轮发生的裂纹、磨损故障进行了特征提取、区分与诊断,并与正常齿轮进行了对比,分析模拟和实验结果表明,效果良好,识别诊断的精度在90%以上,为机械齿轮传动系统的故障识别与诊断提供了一种有效方法。     

The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform

The vibration signal of a gear system is selected as the original information of fault diagnosis and the gear system vibration equipment is established. The vibration acceleration signals of the normal gear, gear with tooth root crack fault, gear with pitch crack fault, gear with tooth wear fault and gear with multi-fault (tooth root crack & tooth wear fault) is collected in four kinds of speed conditions such as 300 rpm, 900 rpm, 1200 rpm and 1500 rpm. Using the method of wavelet threshold de-noising to denoise the original signal and decomposing the denoising signal utilizing the wavelet packet transform, then 16 frequency bands of decomposed signal are got. After restructuring the decomposing signal and obtaining the signal energy in each frequency band, the signal energy of the 16 bands is as the shortlisted fault characteristic data. Based on this, using the methods of principal component analysis (short for PCA) and kernel principal component analysis (short for KPCA) to extract the feature from the fault features of shortlisted 16-dimensional data feature, then the effect of reducing dimension analysis are compared. The fault classifications are displayed through the information that got from the first and the second principal component and kernel principal component, and these demonstrate they have a different and good effect of classification. Meanwhile, the article discusses the effect of feature extraction and classification that caused by the kernel function and the different options of its parameters. These provide a new method for a gear system fault feature extraction and classification.     

薄宽带钢横向瓢曲行为的解析研究

基于艾利应力函数和S. Timoshenko最小功原理建立了薄宽带钢横向瓢曲变形的全新力学分析模型和计算方法,获得了其前屈曲应力场分布的表达式,并同时运用伽辽金虚位移原理解法获得了其临界屈曲载荷;进一步推导得到了不均匀载荷作用下薄宽带钢横向瓢曲变形的后屈曲摄动求解方法及相应数学表达式,计算获得的后屈曲模态与局部拉伸实验得到的瓢曲模态符合良好。     

随机共振消噪和局域均值分解在轴承故障诊断中的应用

针对实际机械故障诊断中强噪声背景下难以提取故障特征的情况,提出了一种基于随机共振消噪(SR)和局域均值分解(LMD)的轴承故障诊断方法。首先,将轴承振动信号进行随机共振消噪,利用噪声增强振动信号的信噪比;然后,将消噪的信号再进行LMD分解,通过求取乘积函数(PF)幅值谱从而发现轴承故障频率。实验结果表明,该方法可以提高信噪比,实现微弱信号的检测,可有效地应用于轴承的故障诊断。
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NEW METHOD OF EXTRACTING WEAK FAILURE INFORMATION IN GEARBOX BY COMPLEX WAVELET DENOISING

Because the extract of the weak failure information is always the difficulty and focus of fault detection. Aiming for specific statistical properties of complex wavelet coefficients of gearbox vibration signals, a new signal-denoising method which uses local adaptive algorithm based on dual-tree complex wavelet transform （DT-CWT） is introduced to extract weak failure information in gear, especially to extract impulse components. By taking into account the non-Gaussian probability distribution and the statistical dependencies among wavelet coefficients of some signals, and by taking the advantage of near shift-invariance of DT-CWT, the higher signal-to-noise ratio （SNR） than common wavelet denoising methods can be obtained. Experiments of extracting periodic impulses in gearbox vibration signals indicate that the method can extract incipient fault feature and hidden information from heavy noise, and it has an excellent effect on identifying weak feature signals in gearbox vibration signals.     

齿轮裂纹故障仿真计算与诊断

提出了一种利用仿真信号对齿轮裂纹故障进行诊断的方法。从齿轮的单自由度振动模型出发,将裂纹故障等效为模型中轮齿刚度的削减,运用差分算法对模型进行求解,得到齿轮的振动位移、速度以及加速度响应,利用傅立叶变换和双谱分析对仿真结果进行处理,成功地提取了齿轮裂纹的故障信息。     

基于线调频小波路径追踪阶比能量解调的齿轮轮齿裂纹故障诊断

针对变速下齿轮裂纹故障信号微弱,难以提取这一特点,提出了基于线调频小波路径追踪的阶比能量解调算法,并将其应用于变速下的齿轮裂纹故障诊断。该方法先采用线调频小波路径追踪算法提取齿轮的啮合频率分量,由此得到转速信号;然后利用转速信号对原始信号进行等角度采样得到角域平稳信号;接着对角域平稳信号进行带通滤波和角域平均运算以消除干扰噪声的影响;最后使用能量算子解调求取瞬时频率和瞬时幅值,根据瞬时频率和瞬时幅值进行故障诊断。应用实例表明,该方法能有效地提取变速下的齿轮裂纹故障。     

基于自相关分析和LMD的滚动轴承振动信号故障特征提取

滚动轴承的故障信号是非平稳的、多分量的调制信号,特别是故障早期,由于调制源弱,早期故障信号微弱且受周围设备的噪声干扰,导致故障特征难以识别。采用自相关分析和局域均值分解（LMD）方法提取故障特征。首先采用自相关分析提取信号中的周期成分,消除噪声的干扰,然后利用局域均值分解方法将多分量的调制信号分解为若干个PF分量之和,再结合共振解调技术对PF分量进行包络分析以提取故障特征频率。实验证明了方法的有效性。