A novel ball bearing fault diagnosis approach based on auto term window method

A new bearing fault diagnosis method based on auto term window (ATW) method is proposed in this paper. Ball bearing is the foremost important and also much easier to be damaged component in the rotation machinery. Vibration signature analysis of machine components is a commonly fault detect technique employed in ball bearing systems. The new fault diagnosis method proposed in this paper is applied to extract fault features in bearing vibration signals. On the base of the Wigner–Ville distribution (WVD) analysis of the bearing signal, the ATW method can not only suppress cross terms effectively, but also strengthen the energy of the auto terms and enhance the feature extraction effect, which is important in the following fault diagnosis research. The ball bearing fault experiment results proved the effectiveness of the ATW method in the last section of this paper.     

基于一种新的时频分布的机械故障诊断

在巧妙构思核函数的基础上,给出了一种新的时频分布(TFC)及其离散算法,并将该TFD应用于机械故障诊断。结合两个诊断实例,同时与Winger-Ville分布(WVD)进行对比,发现该分布具有良好的时频聚集性,并且能够有效的抑制交叉项。事实证明,该分布能够刻画出传统Fourier变换和WVD所不能反映的故障特征信号,能够较好的进行故障诊断。     

基于WVD谱峭度法在轴承故障诊断中的应用

在对基于短时傅里叶变换(STFT)和基于小波变换的谱峭度法分析的基础上,提出了基于W igner Vil le分布的谱峭度法。将其作为检测工具,利用谱峭度构造最优滤波器提取轴承故障 信息。将这三种谱峭度法应用于滚动轴承故障诊断中进行对比分析。分析结果表明，时频分析方法对信号能量的集中程度和时窗与滤波器的选取是影响谱峭度法应用效果的主要因素。该结果对基于时频分析的谱峭度法理论体系的形成及其在故障诊断中的应用具有实际意义。     

基于混合时频分析方法的风电机组故障诊断

针对风电机组传动链系统振动信号非高斯、非平稳性的特点,提出了一种基于混合时频分析的风电机组故障诊断方法。该方法首先采用参数优化Morlet小波消噪方法对原始振动信号进行分析,滤除强大的背景噪声干扰;进而通过自项窗方法抑制时频面的干扰项,增强信号特征成分,提取故障特征以实现故障诊断。在Morlet小波参数优化过程中,采用交叉验证法优化波形参数及连续小波变换的尺度参数;在自项窗的设计过程中,采用基于平滑伪魏格纳分布的函数进行设计,并通过两次阈值处理以减少运算量、提高运算效率。通过对风电机组监测振动数据分析,证明了该方法可以有效地实现背景噪声的消除和故障诊断。     

Wigner-Ville分布在机械故障诊断中的研究

时频分布从时域特征与频域特征的结合途径揭示了信号的构成本质.文章介绍了基于WignerVille分布(WVD)的故障诊断方法,包括基于核函数抑制交叉项,时频分布与人工神经网络相结合,以及WVD的高阶谱.机械系统故障信号往往是非平稳的,联合时频分布是对故障信号分析的有力工具.WVD很高的能量聚集性和很好的时频分辨率,极大地提高了故障信号特征提取的准确度.     

Fault diagnosis for diesel valve trains based on time–frequency images

In this paper, the Wigner–Ville distributions (WVD) of vibration acceleration signals which were acquired from the cylinder head in eight different states of valve train were calculated and displayed in grey images; and the probabilistic neural networks (PNN) were directly used to classify the time–frequency images after the images were normalized. By this way, the fault diagnosis of valve train was transferred to the classification of time–frequency images. As there is no need to extract further fault features (such as eigenvalues or symptom parameters) from time–frequency distributions before classification, the fault diagnosis process is highly simplified. The experimental results show that the faults of diesel valve trains can be classified accurately by the proposed methods.     

Fault diagnosis of a gearbox based on the analysis of a fractional energy gathering band

To extract the weak fault feature of the accelerating process from a gearbox, a fractional energy gathering band time–frequency aggregated spectrum (FETFAS) is proposed to achieve a fast time–frequency analysis of a large signal and to highlight target components. The best order of the fractional Fourier transform (FRFT) is determined according to the rotating speed signal and transmission ratio. The vibration signal from the accelerating process of a gearbox is processed using the best order FRFT. The energy gathering band (EGB) is determined from the modulus spectrum of the FRFT. Then, the result of the FRFT within the EGB is analyzed using time–frequency analysis, and the energy from this result is aggregated to form the FETFAS. The experimental results show that the method to determine the best order of the FRFT from the rotating speed signal is fast and accurate. The time–frequency analysis of the FRFT’s results in the EGB requires less computation and has a high resolution. The FETFAS has the ability to focus and zoom and is able to highlight the target components and restrain noise. Therefore, the FETFAS is an effective method to extract weak fault feature from the signal of gearbox’s accelerating process.     

Signature analysis of mechanical watch movements

This paper presents a new application of the signature analysis for mechanical watch movements. Contrary to the existing method, it analyzes the time–frequency features of a mechanical watch movement through a combination of two well-known techniques: reassigned time–frequency distributions (RTFD) and finite element analysis (FEA). By mapping the signal into a two-dimensional domain of time and frequency, RTFD reveals the frequency components at different time of the movement, while FEA gives the theoretical frequency response of the movements. By comparing the frequency components at different time of the movements to the theoretical frequency response of the movement, various malfunctions of the movement can then be detected. The effectiveness of the presented method is tested for some specific fault diagnosing examples. For completeness, a brief introduction of RTFD is given in the Appendix.     

Feature extraction of AC square wave SAW arc characteristics using improved Hilbert–Huang transformation and energy entropy

In order to extract the arc feature information related to welding quality in alternating current square wave submerged arc welding (AC Square Wave SAW), an improved Hilbert–Huang transform (HHT) is put forward to investigate the time–frequency distribution of arc current, and the energy entropy is employed to quantitatively judge the arc characteristics. The empirical mode decomposition (EMD) is used to decompose the collected current signal into a number of Intrinsic Mode Functions (IMFs). The method for removing the high frequency and undesirable low-frequency IMFs is proposed by using the correlation coefficient of the IMF and the original signal as criterion, and the valid IMFs are selected for the Hilbert transform and energy entropy calculation. The improved HHT combining with energy entropy can quantitatively describe the time–frequency energy distribution characteristics of the arc current signal at different duty cycle, frequency and welding speed. Experimental results are provided to confirm the effectiveness of this approach to extract the arc physical information related to welding quality.     

基于Wigner-Ville分布和Hilbert变换相结合的降噪解调法及应用研究

提出采用Wigner-Ville分布(WVD)和Hilbert变换相结合的办法。首先指出调制信 号WVD中零频处的交叉项包含有原信号的所有调制信息;然后,从理论上分析了它抵消平稳随机 过程中与信号不相关的加性随机噪声的机理;最后对其进行Hilbert变换,求得解调结果。仿真和 实验数据分析证明,该方法与直接解调法相比,噪声影响大幅减小,故障信息得以凸现。     

Modified Laser Speckle Imaging with Adaptive Window

One of the advantages of laser speckle is detecting micro-vascular through image processing. This paper proposes a new image processing method for laser speckle, adaptive window method that adaptively processes laser speckle images in the space. Disadvantage of conventional fixed window method is that it uses the same window size regardless of target areas. Inherently laser speckle contains undesired noise. Thus a large window is helpful for removing the noise, but it results in low resolution of image. Otherwise a small window may detect micro vascular but it has limits in noise removal. To overcome this trade-off, the concept of adaptive window method is newly introduced to conventional laser speckle image analysis. In addition, the modified adaptive window method applied to other selection images. We have compared conventional Laser Speckle Contrast Analysis(LASCA) and its variants with the proposed method in terms of image quality and processing complexity. Moreover compared the result of the accompanied changing selection images have also been compared.     

内燃机变分模态Rihaczek谱纹理特征识别诊断

针对内燃机故障诊断中振动响应信号强耦合、弱故障特征的问题,提出一种基于内燃机振动谱图纹理特征提取的故障诊断方法。首先,为了清晰地刻画内燃机振动信号时频联合分布中的非平稳时变分量,将变分模态分解(VMD)与Rihaczek复能量密度分布方法有效结合,得到了时频聚集性好、无交叉项干扰的内燃机振动谱图像;针对VMD分解过程中的参数选取问题,提出将功率谱熵作为目标函数,对VMD的分解参数进行网格寻优,提高了VMD分解的自适应性。为了实现对内燃机振动谱图像的自动识别及故障诊断,提出了改进的局部二值模式(ILBP)方法,用来对振动谱图中蕴含的纹理信息进行分析,提取低维特征参量并采用最近邻分类器对内燃机不同工况的振动谱图像进行模式识别。将该方法应用于内燃机故障诊断实例中,结果表明该方法能有效提取内燃机振动信号中的微弱故障特征,实现内燃机故障的自动诊断。     

Bearing degradation process prediction based on the PCA and optimized LS-SVM model

Bearing degradation process prediction is extremely important in industry. This paper proposed a new method to achieve bearing degradation prediction based on principal component analysis (PCA) and optimized LS-SVM method. Firstly, the time domain, frequency domain, time–frequency domain features extraction methods are employed to extract the original features from the mass vibration signals. However, the extracted original features still with high dimensional and include superfluous information, the multi-features fusion technique PCA is used to merge the original features and reduce the dimension, the typical sensitive features are extracted. Then, based on the extracted features, the LS-SVM model is constructed and trained for bearing degradation process prediction. The pseudo nearest neighbor point method is used to determine the input number of the model. The particle swarm optimization (PSO) is used to selected the LS-SVM parameters. An accelerated bearing run-to-failure experiment was carried out, the results proved the effectiveness of the methodology.     

Time–frequency analysis of tribological systems—part II: tribology of head–disk interactions

Head–disk interface processes operating in contact and near contact recording generate signals that have a distinct frequency for short time intervals and these processes are known as non-stationary. Time–frequency representation displays time, frequency, and amplitude to characterize such processes. Examples drawn from practical head–disk interface signals are analyzed by adapting the fast Fourier transform algorithm to illustrate the dynamic features jointly in time and frequency. Time–frequency analysis of laser Doppler vibrometer (LDV), friction, and acoustic emission (AE) signals give evidence of non-stationary signals obtained from head–disk dynamics experiments. Novel results depicted by the time–frequency analysis technique not reported elsewhere demonstrate the benefit and usefulness of the proposed techniques.     

一种近距离物体优先的对焦窗口选择策略

随着数码相机普遍进入千万级有效像素的行列,大多数相机都采用了多个对焦窗口的自动对焦技术。为实现对焦窗口的快速选择和自动对焦,文中采用统计分析的方法,研究了近距离物体优先的对焦窗口自动选择的可行性,就选择适宜的对焦评价函数提出建议;通过MatLab仿真实验给出了评价函数的大小阈值,提出了近距离物体优先的对焦窗口自动选择和自动对焦策略,给出了对焦搜索基本流程。这种对焦窗口选择,实时性好,适用性强,可作为数码相机的一种对焦窗口自动选择策略。     

A TIME-FILTERED WIGNER TRANSFORMATION FOR USE IN SIGNAL ANALYSIS

Two important goals of time–frequency transformations in signal analysis are high time and frequency resolution and simple interpretableness. The Wigner transformation exceeds the frequently used spectrogram with respect to the resolutions, it possesses, however, interference terms, which makes the interpretation difficult. In this paper a procedure is presented, with which by repeated filtering in time domain and subsequent Wigner transformation a transformation result is achieved, in which the time–frequency resolution of the Wigner transformation is maintained and at the same time the interference terms can be removed. By a practical example, the advantages of this procedure are clarified in relation to the spectrogram and the original Wigner transformation.     

Time–frequency approach to extraction of selected second-order cyclostationary vibration components for varying operational conditions

In this paper, the authors propose a method for extracting second-order cyclostationary components from a vibration signal. For a known cyclic frequency, the proposed algorithm allows to estimate the amount of energy of each cyclic component of interest in the time–frequency domain. In this way, the resulting representation contains only the chosen second-order cyclostationary component that manifests itself as a number of carrier frequencies modulated by the harmonic signal of selected frequency.     

基于Wigner高阶谱的机械故障诊断的研究

Wigner高阶谱是Wigner-ville分布到高阶矩谱域的扩展,可同时从时域和频域描述信号的高阶谱特征,是分析时变的非高斯信号的有力工具。将Wigner高阶谱应用到机械故障诊断中,研究了交叉干扰项的消除和抑噪能力。仿真和试验结果表明该方法是有效的,得到了一些有价值的结论。     

HHT-based nonlinear signal processing method for parametric and non-parametric identification of dynamical systems

This paper presents a time–frequency signal processing method based on Hilbert–Huang transform (HHT) and a sliding-window fitting (SWF) technique for parametric and non-parametric identification of nonlinear dynamical systems. The SWF method is developed to reveal the limitations of conventional signal processing methods and to perform further decomposition of signals. Similar to the short-time Fourier transform and wavelet transform, the SWF uses windowed regular harmonics and function orthogonality to extract time-localized regular and/or distorted harmonics. On the other hand, HHT uses the apparent time scales revealed by the signal's local maxima and minima to sequentially sift components of different time scales, starting from high- to low-frequency ones. Because HHT does not use pre-determined basis functions and function orthogonality for component extraction, it provides more accurate time-varying amplitudes and frequencies of extracted components for accurate estimation of system characteristics and nonlinearities. Methods are developed to reduce the end effect caused by Gibbs’ phenomenon and other mathematical and numerical problems of HHT analysis. For parametric identification of a nonlinear one-degree-of-freedom system, the method processes one free damped transient response and one steady-state response and uses amplitude-dependent dynamic characteristics derived from perturbation analysis to determine the type and order of nonlinearity and system parameters. For non-parametric identification, the method uses the maximum displacement states to determine the displacement–stiffness curve and the maximum velocity states to determine the velocity-damping curve. Moreover, the SWF method and a synchronous detection method are used for further decomposition of components extracted by HHT to improve the accuracy of parametric and non-parametric estimations. Numerical simulations of several nonlinear systems show that the proposed method can provide accurate parametric and non-parametric identifications of different nonlinear dynamical systems.