基于FRFT的单分量阶比双谱提取微弱故障特征

提出了一种基于分数阶傅里叶变换(fractional Fourier transform,简称FRFT)的单分量阶比双谱分析方法,消除阶比双谱分析多分量信号时产生的交叉项,提取变速器齿轮微弱故障特征。根据变速器输入轴转速信号及传动比确定FRFT最佳阶次,对变速器升速过程振动信号进行最佳阶次FRFT,在该分数阶域分离目标阶比分量,对分离出的单分量信号分别进行阶比双谱分析,并累加各分量阶比双谱结果得到基于FRFT的单分量阶比双谱。试验结果表明,变速器变速过程振动信号为多阶比分量信号,直接对其进行阶比双谱分析会产生明显的交叉项,使阶比双谱阶次和幅值失真。基于FRFT的单分量阶比双谱方法能有效屏蔽其他分量和噪声干扰、消除交叉项,真实、准确反映被分析信号的阶比双谱,有效提取变速器齿轮微弱故障特征。     

切片双谱分析在离心泵故障诊断中的应用

运用双谱及切片双谱分析技术对离心泵不同状态下的振动信号进行分析表明,离心泵在正常运行、地脚螺栓松动和空化状态下的双谱图的特征有着明显的差别,可以通过双谱对故障信号进行初步分类,且双谱的对角切片和反对角切片可以显著降低噪声干扰,进一步提取出故障特征频率,故可对离心泵的故障信号进行准确的分类和诊断。     

基于瞬时转速双谱分析的柴油机气阀泄漏诊断方法

在对双谱和瞬时转速信号的特点进行分析的基础上,测量了6-135柴油机在正常和气阀泄漏故障状态下的瞬时转速,分别计算其双谱,得到了具有明显区别的双谱图;通过计算双谱对角切片,可以容易且有效地识别故障的存在;根据瞬时转速的双谱特征进行故障诊断,故障特征明显,诊断效果良好。     

Fault Feature Extraction of Diesel Engine Based on Bispectrum Image Fractal Dimension

Fault feature extraction has a positive effect on accurate diagnosis of diesel engine. Currently, studies of fault feature extraction have focused on the time domain or the frequency domain of signals. However, early fault signals are mostly weak energy signals, and time domain or frequency domain features will be overwhelmed by strong back?ground noise. In order consistent features to be extracted that accurately represent the state of the engine, bispectrum estimation is used to analyze the nonlinearity, non?Gaussianity and quadratic phase coupling(QPC) information of the engine vibration signals under different conditions. Digital image processing and fractal theory is used to extract the fractal features of the bispectrum pictures. The outcomes demonstrate that the diesel engine vibration signal bispectrum under different working conditions shows an obvious differences and the most complicated bispectrum is in the normal state. The fractal dimension of various invalid signs is novel and diverse fractal parameters were utilized to separate and characterize them. The value of the fractal dimension is consistent with the non?Gaussian intensity of the signal, so it can be used as an eigenvalue of fault diagnosis, and also be used as a non?Gaussian signal strength indicator. Consequently, a symptomatic approach in view of the hypothetical outcome is inferred and checked by the examination of vibration signals from the diesel motor. The proposed research provides the basis for on?line monitoring and diagnosis of valve train faults.     

基于双谱的汽油机爆震特征提取与强度评价

将双谱分析应用于爆震振动信号分析,提出了爆震特征频率提取和强度判定的方法。首先,用功率谱密度估计的方法分析发动机3个方向的振动信号,确定爆震特征频率范围;然后,利用双谱分析不同爆震强度下的振动信号并提取双谱主对角线切片,分析不同状况下的信号频率间的相位耦合关系,确定爆震特征频率;最后,提出了爆震强度评价参数。试验结果表明,相比功率谱密度估计,双谱峰值频率更能凸显爆震特征频率,爆震强度评价参数能有效判定爆震强度。     

基于声发射的双谱分析在金刚笔状态特征提取中的应用

为了有效地识别砂轮修整过程中金刚笔的钝化状态，针对金刚笔修整砂轮过程中声发射信号的非平稳时变特点，研究了用于金刚笔状态特征提取的基于声发射的双谱分析方法。通过砂轮修整过程在线监测实验，分析了金刚笔在锐利、中等钝化和钝化状态下，修整过程中声发射信号的双谱特征，提出了归一化双谱模对角切片特征提取方法。研究表明，采用基于声发射的双谱分析方法可以有效监测砂轮修整过程中金刚笔的钝化状态。     

局部积分双谱及其在齿轮故障诊断中的应用

提出了一种局部积分双谱分析方法,探讨了局部积分双谱抑制噪声的能力,利用局部积分双谱分析了正常齿轮和早期剥落齿轮振动信号。局部积分双谱可以分析出齿轮故障的调制现象,结果显示,局部积分双谱与传统的双谱切片相比能较全面地反映双谱信息,是处理齿轮故障调制现象的有力工具。     

变速器状态监测及故障诊断方法

汽车变速箱是汽车的主要传动部件,准确地监测变速器运行状态和提前预估试验中可能出现的故障,对于产品改进、试验设备及人员安全及其重要。分析变速器各种零件振动信号的特征,建立基于状态监测和故障诊断的分析模型和状态监测方案。该方案可从复合振动信号中分离出齿轮的振动信号、轴不对中故障振动信号和轴承故障振动信号,能有效地诊断变速器内部零件的典型故障。     

利用神经网络对齿轮箱进行故障诊断的实例分析

以JZQ250型号齿轮箱为实验对象,根据实际状况下齿轮箱的故障机理和振动特点,设计了状态监测与故障诊断实验方案,采集了齿轮箱振动信号,应用MATLAB软件中自带的神经网络工具箱,编程实现了神经网络预测模型.仿真和实验证明,此模型能够有效地检测出齿轮箱的典型故障,可以用于齿轮箱的故障诊断.     

Monitoring gear vibrations through motor current signature analysis and wavelet transform

In gearboxes, load fluctuations on the gearbox and gear defects are two major sources of vibration. Further, at times, measurement of vibration in the gearbox is not easy because of the inaccessibility in mounting the vibration transducers. An efficient and new but non-intrusive method to detect the fluctuation in gear load may be the motor current signature analysis (MCSA). In this paper, a multi-stage transmission gearbox (with and without defects) has been studied in order to replace the conventional vibration monitoring by MCSA. It has been observed through FFT analysis that low frequencies of the vibration signatures have sidebands across line frequency of the motor current whereas high frequencies of vibration signature are difficult to be detected. Hence, discrete wavelet transform (DWT) is suggested to decompose the current signal, and FFT analysis is carried out with the decomposed current signal to trace the sidebands of the high frequencies of vibration. The advantage of DWT technique to study the transients in MCSA has also been cited. The inability of CWT in detecting either defects or load fluctuation has been shown. The results indicate that MCSA along with DWT can be a good replacement for conventional vibration monitoring.     

齿轮早期疲劳裂纹的混沌检测方法

齿轮箱振动信号中调制现象普遍存在,而且啮合频率产生的周期冲击成分占很大比重,反映齿轮箱故障的特征信号的幅值相对较低,难以检测。根据齿轮箱振动信号的特点,提出了基于混沌振子的齿轮早期疲劳裂纹检测方法,区别于目前常用的基于混沌振子的微弱信号检测方法。该方法通过辨识混沌振子加入齿轮箱振动信号后发生的由大尺度周期状态到混沌状态的反向状态改变,确定齿轮啮合频率边频带的状态,从而判断齿轮裂纹的发展情况,在齿轮裂纹的监测中取得了良好的效果。     

FAULT DIAGNOSTICS USING STATISTICAL CHANGE DETECTION IN THE BISPECTRAL DOMAIN

It is widely accepted that structural defects in rotating machinery components (e.g. bearings and gears) can be detected through monitoring of vibration and/or sound emissions. Traditional diagnostic vibration analysis attempts to match spectral lines with a priori -known defect frequencies that are characteristic of the affected machinery components. Emphasis herein is on use of bispectral-based statistical change detection algorithms for machinery health monitoring. The bispectrum, a third-order statistic, helps identify pairs of phase-related spectral components, which is useful for fault detection and isolation. In particular, the bispectrum helps sort through the clutter of usual (second-order) vibration spectra to extract useful information associated with the health of particular components. Seeded and non-seeded helicopter gearbox fault results (CH-46E and CH-47D, respectively) show that bispectral algorithms can detect faults at the level of an individual component (i.e. bearings or gears). Fault isolation is implicit with detection based on characteristic a priori -known defect frequencies. Important attributes of the bispectral SCD approach include: (1) it does not require a priori training data as is needed for traditional pattern-classifier-based approaches (and thereby avoids the significant time and cost investments necessary to obtain such data); (2) being based on higher-order moment-based energy detection, it makes no assumptions about the statistical model of the bispectral sequences that are generated; (3) it is operating-regime independent (i.e. works across different operating conditions, flight regimes, torque levels, etc., without knowledge of same); (4) it can be used to isolate faults to the level of specific machinery components (e.g. bearings and gears); and (5) it can be implemented using relatively inexpensive computer hardware, since only low-frequency vibrations need to be processed. The bispectral SCD algorithm thus represents a general methodology for the automated analysis of rotating machinery.     

基于多重分形与SVM的齿轮箱故障诊断研究

针对齿轮箱振动信号的非平稳性和非线性,提出一种多重分形和支持向量机相结合的故障诊断方法。运用多重分形理论方法对齿轮振动信号进行分析,通过分析发现多重分形谱和广义维数作为故障特征能够很好地反映齿轮箱的工作状态;对支持向量机的参数利用粒子群优化算法进行优化,并将齿轮箱振动信号的多重分形特征量作为支持向量机的输入参数以识别齿轮的故障类型。实验结果表明,该方法在样本较小的情况下能够准确对齿轮箱的故障类型进行分类。     

Subspace-based gearbox condition monitoring by kernel principal component analysis

Feature extraction is a key step for gearbox condition monitoring. The statistical features of the measured vibrations can be used to characterise gearbox conditions; however, their regularity and sensitivity in pattern space are different and may vary considerably under different operating conditions. This paper addresses the non-linear feature extraction scheme from the time-domain features with wavelet packet preprocessing and frequency-domain features of the vibration signals using the kernel principal component analysis (KPCA). Then two different KPCA-based subspace structures are constructed for representing and classifying the gearbox conditions. The proposed methods can extract the non-linear features of gearbox conditions using KPCA effectively, and perform conveniently with low computational complexity based on subspace methods. Experimental analysis with a fatigue test of an automobile transmission gearbox shows that the KPCA features outperform PCA features in terms of clustering capability, and both the two KPCA-based subspace methods can be effectively applied to gearbox condition monitoring.     

基于线调频小波路径追踪阶比循环平稳解调的齿轮故障诊断

为从变转速齿轮箱振动信号中提取齿轮故障特征,提出基于线调频小波路径追踪的阶比循环平稳解调方法。该方法利用线调频小波路径追踪算法估计振动信号中的转速信号,根据转速信号对信号进行等角度采样,获取角域周期平稳信号,求取角域信号的循环自相关函数,在特征循环阶比处对循环自相关函数进行切片,并对切片进行解调分析得到切片解调谱,依据切片解调谱进行齿轮故障诊断。由于线调频小波路径追踪算法具有精度高和抗噪能力强的优点,而循环平稳解调算法可以有效提取淹没在噪声中的周期性故障特征,因而,该方法结合了二者的优点,适合于变转速齿轮信号的故障特征提取。算法仿真和应用实例表明,该方法能有效地提取变转速齿轮箱振动信号中的齿轮故障特征。     

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.     

Vibration condition monitoring of planetary gearbox under varying external load

The paper shows that for condition monitoring of planetary gearboxes it is important to identify the external varying load condition. In the paper, systematic consideration has been taken of the influence of many factors on the vibration signals generated by a system in which a planetary gearbox is included. These considerations give the basis for vibration signal interpretation, development of the means of condition monitoring, and for the scenario of the degradation of the planetary gearbox. Real measured vibration signals obtained in the industrial environment are processed. The signals are recorded during normal operation of the diagnosed objects, namely planetary gearboxes, which are a part of the driving system used in a bucket wheel excavator, used in lignite mines. It is found that a planetary gearbox in bad condition is more susceptible to load than a gearbox in good condition. The estimated load time traces obtained by a demodulation process of the vibration acceleration signal for a planetary gearbox in good and bad conditions are given. It has been found that the most important factor of the proper planetary gearbox condition is connected with perturbation of arm rotation, where an arm rotation gives rise to a specific vibration signal whose properties are depicted by a short-time Fourier transform (STFT) and Wigner-Ville distribution presented as a time–frequency map. The paper gives evidence that there are two dominant low-frequency causes that influence vibration signal modulation, i.e. the varying load, which comes from the nature of the bucket wheel digging process, and the arm/carrier rotation. These two causes determine the condition of the planetary gearboxes considered. Typical local faults such as cracking or breakage of a gear tooth, or local faults in rolling element bearings, have not been found in the cases considered. In real practice, local faults of planetary gearboxes have not occurred, but heavy destruction of planetary gearboxes have been noticed, which are caused by a prolonged run of a planetary gearbox at the condition of the arm run perturbation. It may be stated that the paper gives a new approach to the condition monitoring of planetary gearboxes. It has been shown that only a root cause analysis based on factors having an influence on the vibration solves the problem of planetary gearbox condition monitoring.     

Bispectrum feature extraction of gearbox faults based on nonnegative Tucker3 decomposition with 3D calculations

Nonnegative Tucker3 decomposition（NTD） has attracted lots of attentions for its good performance in 3D data array analysis. However, further research is still necessary to solve the problems of overfitting and slow convergence under the anharmonic vibration circumstance occurred in the field of mechanical fault diagnosis. To decompose a large-scale tensor and extract available bispectrum feature, a method of conjugating Choi-Williams kernel function with Gauss-Newton Cartesian product based on nonnegative Tucker3 decomposition（NTD_EDF） is investigated. The complexity of the proposed method is reduced from o（nNlgn） in 3D spaces to o（RiR2nlgn） in 1D vectors due to its low rank form of the Tucker-product convolution. Meanwhile, a simultaneously updating algorithm is given to overcome the overfitting, slow convergence and low efficiency existing in the conventional one-by-one updating algorithm. Furthermore, the technique of spectral phase analysis for quadratic coupling estimation is used to explain the feature spectrum extracted from the gearbox fault data by the proposed method in detail. The simulated and experimental results show that the sparser and more inerratic feature distribution of basis images can be obtained with core tensor by the NTD EDF method compared with the one by the other methods in bispectrum feature extraction, and a legible fault expression can also be performed by power spectral density（PSD） function. Besides, the deviations of successive relative error（DSRE） of NTD_EDF achieves 81.66 dB against 15.17 dB by beta-divergences based on NTD（NTD_Beta） and the time-cost of NTD EDF is only 129.3 s, which is far less than 1 747.9 s by hierarchical alternative least square based on NTD （NTD_HALS）. The NTD_EDF method proposed not only avoids the data overfitting and improves the computation efficiency but also can be used to extract more inerratic and sparser bispectrum features of the gearbox fault.     

基于非平稳时间序列双标度指数特征的齿轮箱故障诊断

齿轮箱振动信号是含噪的多分量非平稳信号,故障模式相近的复杂齿轮箱振动信号的分类是齿轮箱故障诊断的难点。采用去趋势波动分析(Detrended fluctuation analysis,DFA)方法分析齿轮箱振动信号的标度行为,随着时间尺度的增大,标度指数会突然发生改变,形成双标度指数特征,由双标度指数构成的二维矢量具有明确的物理意义,可以作为表征时间序列内部动力学机制的特征参数。提出一种基于非平稳时间序列双标度指数特征的齿轮箱故障诊断方法,对正常状态和含有轻度磨损、中度磨损和断齿故障的齿轮箱故障诊断的结果表明,提出的方法成功地解决了故障模式相近的复杂齿轮箱振动信号的分类问题,很好地克服了傅里叶变换、小波变换和基于单标度指数的方法存在的缺陷,为齿轮箱的故障诊断提供了一种新的方法。