奇异值差分谱理论及其在车床主轴箱故障诊断中的应用

证明采用Hankel矩阵时奇异值分解(Singular value decomposition,SVD)可以将信号分解为一系列分量信号的简单线性叠加,为了确定其中的有用分量个数,提出奇异值差分谱的概念。差分谱可以有效地描述有用分量和噪声分量的奇异值性质差异,根据差分谱峰值位置可实现对有用分量个数的确定。研究结果表明,当差分谱最大峰值位于第一个坐标时,则表明原始信号存在较大的直流分量,此时根据第二最大峰值位置可以确定有用分量的个数,否则就根据最大峰值位置来确定分量个数。利用差分谱进一步研究Hankel矩阵的结构对SVD降噪效果的影响,指出矩阵列数和噪声去除量存在抛物线状的对称关系。利用基于差分谱的SVD方法对车削力信号进行处理,结果有效地分离出由于主轴箱故障齿轮的振动而引起的调制信号,并根据此信号可靠地定位了故障齿轮。     

De-noising of diesel vibration signal using wavelet packet and singular value decomposition

The vibration signals of diesel include excess noise that must be eliminated before extraction of characteristic parameters. Firstly, the effects of vibration-signal de-noising among Fourier transform, wavelet decomposition and wavelet packet decomposition are compared. Secondly, singular value decomposition is applied to de-noising vibration signals. Finally, a new de-noise method integrated with wavelet packet and singular value is presented. In this method, vibration signals are decomposed by wavelet packet, and the wavelet packet coefficient is de-noised by singular value decomposition again. The results indicate that the new de-noising method is the best. The SNR (signal-to-noise ratio) of the vibration signals of a diesel cylinder lid is the highest. The diesel vibration waveforms of combustion and valve become clear and the extracted characteristic parameters become more precise. __________ Translated from Journal of China University of Petroleum (Natural Science Edition), 2006, 30(1) (in Chinese)     

基于VMD的Volterra模型奇异值熵的转子故障诊断方法

针对转子故障信号的非平稳性以及敏感故障特征无法有效提取的问题,将变分模态分解(variational mode decomposition, VMD)的Volterra模型和奇异值熵相结合,提出一种故障诊断方法。对影响VMD分解准确性的参数选取方法进行了深入研究,给出了相关问题的解决策略。首先,对不同工况下转子实测信号进行VMD分解,利用能量熵增量选取对故障特征敏感的固有模态函数(intrinsic mode function, IMF)进行相空间重构,以建立Volterra自适应预测模型,将模型参数作为初始特征向量矩阵。然后,对初始特征向量进行奇异值分解以获取奇异值熵和奇异值特征向量矩阵,用于描述转子的故障特征。最后,采用模糊C均值(fuzzy c-means, FCM)算法对转子工作状态和故障类型进行识别。试验结果表明,所提方法可有效实现转子故障的特征提取及类型识别。通过同经集合经验模态分解(ensemble empirical mode decomposition, EEMD)相比,证明了该方法具有更有效的故障特征提取性能,是一种可行的方法。     

The envelope order spectrum based on generalized demodulation time–frequency analysis and its application to gear fault diagnosis

The generalized demodulation time–frequency analysis is a novel signal processing method, which is particularly suitable for the processing of multi-component amplitude-modulated and frequency-modulated (AM–FM) signals as it can decompose a multi-component signal into a set of single-component signals whose instantaneous frequencies own physical meaning. While fault occurs in gear, the vibration signals measured from gearbox would exactly display AM–FM characteristics. Therefore, targeting the modulation feature of gear vibration signal in run-ups and run-downs, a fault diagnosis method in which generalized demodulation time–frequency analysis and envelope order spectrum technique are combined is put forward and applied to the transient analysis of gear vibration signal. Firstly the multi-component vibration signal of gear is decomposed into some mono-component signals using the generalized demodulation time–frequency analysis approach; secondly the envelope analysis is performed to each single-component signal; thirdly each envelope signal is re-sampled in angle domain; finally the spectrum analysis is applied to each re-sampled signal and the corresponding envelope order spectrum can be obtained. Furthermore, the gear working condition can be identified according to the envelope order spectrum. The analysis results from the simulation and experimental signals show that the proposed algorithm was effective in gear fault diagnosis.     

基于EMD和AR奇异值的柴油机故障诊断

针对柴油机振动信号的非平稳特性和在现实条件下难以获取大量故障样本的实际情况,提出一种经验模态分解(Empirical Mode Decomposition,EMD)、自回归(Auto Regression,AR)模型和支持向量机(Support Vector Machine,SVM)相结合的柴油机故障诊断方法.运用经验模态分解方法对柴油机失火及气阀机构不同工况下的缸盖振动信号进行分析,计算各个内禀模态函数(Intrinsic Mode Functions,IMF)的AR模型参数向量以此组成初始特征向量矩阵,再计算此初始特征向量矩阵的奇异值,并将其作为支持向量机的输入特征向量以判断柴油机的工作状态和故障类型.试验结果表明:该方法在小样本情况下也具有较高的精度和较强的泛化能力.     

An improvement EMD method based on the optimized rational Hermite interpolation approach and its application to gear fault diagnosis

A demodulation technique based on improvement empirical mode decomposition (EMD) is investigated in this paper. Firstly, the problem of the envelope line in EMD is introduced and the drawbacks of two classic interpolation methods, cubic spline interpolation method and cubic Hermite interpolation method are discussed; then a new envelope interpolation method called optimized rational Hermite interpolation method (O-EMD) is proposed, which has a shape controlling parameter compared with the cubic Hermite interpolation algorithm. At the same time, in order to improve the envelope approximation accuracy of local mean, the parameter determining criterion is put forward and an optimization with Genetic Algorithm (GA) is applied to automatic select the suitable shape controlling parameter in each sifting process. The effectiveness of O-EMD method is validated by the numerical simulations and an application to gear fault diagnosis. Results demonstrate that O-EMD method can improve the reliability and accuracy significantly compared with traditional EMD method.     

Sparse signal decomposition method based on multi-scale chirplet and its application to the fault diagnosis of gearboxes

Based on the chirplet path pursuit and the sparse signal decomposition method, a new sparse signal decomposition method based on multi-scale chirplet is proposed and applied to the decomposition of vibration signals from gearboxes in fault diagnosis. An over-complete dictionary with multi-scale chirplets as its atoms is constructed using the method. Because of the multi-scale character, this method is superior to the traditional sparse signal decomposition method wherein only a single scale is adopted, and is more applicable to the decomposition of non-stationary signals with multi-components whose frequencies are time-varying. When there are faults in a gearbox, the vibration signals collected are usually AM-FM signals with multiple components whose frequencies vary with the rotational speed of the shaft. The meshing frequency and modulating frequency, which vary with time, can be derived by the proposed method and can be used in gearbox fault diagnosis under time-varying shaft-rotation speed conditions, where the traditional signal processing methods are always blocked. Both simulations and experiments validate the effectiveness of the proposed method.     

An ensemble local means decomposition method and its application to local rub-impact fault diagnosis of the rotor systems

Targeting the shock characteristics of the vibration signal of a rotor system with local rub-impact fault, a local rub-impact fault diagnosis method of rotor system based on ELMD (ensemble local means decomposition) is proposed in this paper. The local mean decomposition (LMD) is a newly self-adaptive time-frequency analysis method, by which any complicated multi-component signal could be decomposed into a set of product functions (PFs) whose instantaneous frequencies in theory have physical significance. Unfortunately, mode mixing phenomenon which makes the decomposition results devoid of physical meaning is common when LMD is performed in practice. Targeting this shortcoming, the filter bank structure of white noise by LMD is obtained by numerical experiments, and then an improved method based upon noise-assisted analysis, ensemble local mean decomposition, is put forward. In ELMD, firstly, different white noise is added to the targeted signal; secondly, LMD is used to decompose the noise-added signal into product functions (PFs); finally, the ensemble means of corresponding PF components derived from LMD is regarded as the final decomposition result. The analytical results from simulation signal and experimental rotor local rub-impact signal demonstrate that the ELMD approach can be used to overcome the mode mixing of the original LMD method effectively.     

Detection and recovery of fault impulses via improved harmonic product spectrum and its application in defect size estimation of train bearings

The detection and recovery of impulsive signature play a vital role in the diagnosis and prognosis of rolling element bearings. Though different approaches have been proposed to deal with this problem so far, challenges still exist when they are applied to the bearings operating under harsh working conditions. The difficulties mainly come from the multi-resonance and multi-modulation characteristics of bearing vibration signals. To overcome this limitation, a new methodology for the detection and recovery of fault impulses is presented in this paper. First, an improved harmonic product spectrum (IHPS) is proposed to detect and identify the multiple modulation sources buried in a vibration signal. With this method, the fault-related impulsive features could be recognized, while the influence caused by non-fault modulation is eliminated. On this basis, a harmonic significance index is further established to quantify the diagnostic information contained in a narrow band signal. By utilizing this index, the optimal resonance band where the fault impulses are most significant could be accurately determined. Finally, IHPS and sideband product spectrum are integrated to reduce the in-band noise and further recover the fault impulses. The performance of this method is evaluated by both simulated data and real vibration data measured from a train wheel bearing with a naturally developed defect. Compared with Kurtogram and Protrugram, the proposed method can detect the resonance band more precisely even in the presence of heavy noise and other impulsive vibration sources. Moreover, with the impulses recovery scheme, the double impact phenomenon caused by a distributed defect is extracted successfully. Benefiting from this, the defect size of a bearing can be estimated from its vibration signal without dismantling, which makes it a promising tool for the bearing diagnosis and prognosis in industrial applications.     

柴油发动机故障诊断技术研究与应用

应用振动法对柴油机气缸体上的振动信号进行分析,得出总振动量级主随活塞与气缸套磨损间隙的增大而适级放大.根据气缸体振动加速度响应功率谱图和柴油发动机总振动量级,可以确定活塞与气缸壁的间隙大小.应用油样铁谱分析技术,可以确定发动机的润滑状况及摩擦副的磨损程度和部位,并通过实例证明了其在柴油发动机故障诊断中的有效性.应用直读式原子发射光谱仪对柴油发动机润滑油油样进行检测,监控柴油发动机曲轴滑动轴承磨损状况,对保障发动机可靠运行起到很好的作用.     

矢功率谱与蚁群神经网络结合在机械故障诊断中的应用研究

针对传统功率谱信号源不足以及BP神经网络收敛速度慢且容易陷入局部极小等问题,提出矢功率谱和蚁群神经网络相结合的故障诊断方法,该方法是:提取矢功率谱的8个频段能量特征,并输入到蚁群神经网络分类器进行故障识别,通过实际训练结果和实验结果对比可知,蚁群神经网络能有效地提高收敛速度,网络迭代次数明显改善,故障识别率提高,将蚁群神经网络应用于机械故障诊断是有效的.     

An improved complementary ensemble empirical mode decomposition with adaptive noise and its application to rolling element bearing fault diagnosis

A novel time–frequency analysis method called complementary complete ensemble empirical mode decomposition (EEMD) with adaptive noise (CCEEMDAN) is proposed to analyze nonstationary vibration signals. CCEEMDAN combines the advantages of improved EEMD with adaptive noise and complementary EEMD, and it improves decomposition performance by reducing reconstruction error and mitigating the effect of mode mixing. However, because white noise mixed in with the raw vibration signal covers the whole frequency bandwidth, each mode inevitably contains some mode noise, which can easily inundate the fault-related information. This paper proposes a time–frequency analysis method based on CCEEMDAN and minimum entropy deconvolution (MED) for fault detection of rolling element bearings. First, a raw signal is decomposed into a series of intrinsic mode functions (IMFs) by using the CCEEMDAN method. Then a sensitive parameter (SP) based on adjusted kurtosis and Pearson’s correlation coefficient is applied to select a sensitive mode that contains the most fault-related information. Finally, the MED is applied to enhance the fault-related impulses in the selected IMF. The fault signals of high-speed train axle-box bearing are applied to verify the effectiveness of the proposed method. Results show that the proposed method can effectively reveal axle-bearing defects’ fault information. The comparisons illustrate the superiority of SP over kurtosis for selecting the sensitive mode from the resulted signal of CCEEMEDAN. Further, we conducted comparisons that highlight the superiority of our proposed method over individual CCEEMDAN and MED methods and over two other popular signal-processing methods, variational mode decomposition and fast kurtogram.     

基于小波包移频算法的AR功率谱在滚动轴承故障特征信息提取中的应用

为克服经典的小波包迭代算法由于小波包分解过程中的隔点采样而发生的频率混叠现象，本文采用移频算法进行小波包分解与重构，以1#、2#、3#这3个608滚动轴承(其中1#轴承工作正常，而2#、3#轴承工作异常)进行分析。先通过小波包分解提取这3个轴承振动信号的频带能量特征以确定2#、3#轴承故障特征信息所在的频带。并按这些频带分别对2#、3#轴承的振动信号进行小波包重构。通过对重构信号的基于AR模型的功率谱分析以实现滚动轴承故障特征信息的自动提取．从而对2#、3#轴承的故障作出诊断。     

Sync-period frequency analysis and its application to the diagnosis of multiple element defects of rolling bearings

A method for diagnosing multiple element defects in rolling bearings has been investigated. The method combines the time-synchronous averaging and envelope spectral analysis techniques to produce spectra of synchronously averaged envelope signals with a range of synchronous frequencies. The spectra are displayed in the synchronous period versus frequency domain, to result in the sync-period versus frequency distribution. The distribution separates the characteristic defect frequencies and their associated sidebands in the synchronous period axis. This analysis technique makes it possible to detect and diagnose multiple defects appearing in different elements of rolling bearings. Another main benefit of the method is the significant noise reduction by both the enveloping and the synchronous averaging processes. Results from both computer synthesised data and experimental simulated data are presented.