基于EMD的谱峭度方法在滚动轴承故障检测中的应用

传统谱峭度方法通常采用基于短时傅里叶变换(Short Time Fourier Transform,STFT)的峭度图方法来实现。针对STFT不能保证对瞬态脉冲这种高度非平稳信号最优的分解效果的缺点,提出一种基于经验模式分解(Empirical Mode Decomposition ,EMD)的谱峭度方法。该方法首先利用EMD和Hilbert变换得到信号的时频分布,然后将信号的时频分布按照不同层数分成若干频段,通过计算各频段的峭度值得到相应的峭度图,再根据峭度最大原则选择滤波频段进行带通滤波,最后对滤波信号采用包络分析确定故障信息。实验结果表明：相比传统基于STFT的谱峭度方法,本文方法更能准确的获得轴承加速度信号的故障特征频率信息。     

运动声源多普勒畸变信号自适应校正方法EI北大核心CSCD

提出了一种自适应多普勒畸变校正方法,以声源移动速度v、初始时刻麦克风与声源横向距离x两个运动学参数为优化变量,以最大化重采样信号的频域统计指标为优化目标,通过参数寻优进行v和x的估计,通过幅值还原和时域插值拟合进行畸变校正。仿真分析结果表明,频谱峭度、频谱偏度、频谱脉冲因子和频谱峰值因子4种统计指标均能准确识别运动学参数,且频谱峭度的抗噪能力最好,临界信噪比达到-3.1 dB。实验分析结果表明,列车故障轴承多普勒畸变声音信号校正后,包络谱故障频率成分及其倍频成分清晰准确,说明多普勒畸变得到有效校正。该方法可基于信号本身实现多普勒畸变信号时频结构的全面校正。     

Envelope extraction based dimension reduction for independent component analysis in fault diagnosis of rolling element bearing

A robust feature extraction scheme for the rolling element bearing (REB) fault diagnosis is proposed by combining the envelope extraction and the independent component analysis (ICA). In the present approach, the envelope extraction is not only utilized to obtain the impulsive component corresponding to the faults from the REB, but also to reduce the dimension of vibration sources included in the sensor-picked signals. Consequently, the difficulty for applying the ICA algorithm under the conditions that the sensor number is limited and the source number is unknown can be successfully eliminated. Then, the ICA algorithm is employed to separate the envelopes according to the independence of vibration sources. Finally, the vibration features related to the REB faults can be separated from disturbances and clearly exposed by the envelope spectrum. Simulations and experimental tests are conducted to validate the proposed method.     

基于滑动峭度相关性准则的局部特征尺度分解分量筛选方法

轴承故障振动信号具有非平稳、非线性特征,且可视为多个调幅-调频分量的叠加,单分量的包络蕴含了轴承的故障特征。局部特征尺度分解可将振动信号准确分解为多个内禀尺度分量之和,某些分量能清晰反映轴承的运行状态,根据包络谱可进行故障诊断。为了准确筛选有用分量,提出了基于滑动峭度相关性准则的分量筛选方法。首先,对信号进行局部特征尺度分解,得到若干个内禀尺度分量;然后,对分量和原始信号分别计算滑动峭度,生成时间序列;最后,依据分量滑动峭度序列与原始信号滑动峭度序列的互相关系数筛选有用分量。通过轴承内圈故障数据分析发现：有用分量与非有用分量之间的滑动峭度互相关系数比互相关系数差异明显,区分度更大,有益于分量的分类、筛选。     

Identification of multiple transient faults based on the adaptive spectral kurtosis method

This paper proposes a multi-fault detection method based on the adaptive spectral kurtosis (ASK) analysis of the vibration signal from single sensor. A theoretical model of multiple bearing faults is established in this paper. Compared with the kurtogram and protrugram techniques, the proposed method can more effectively extract signatures of multiple bearing faults even in the presence of strong background noise. The performance of the proposed method in fault detection of the rolling element bearings is validated using simulation data and experimental signals from a bearing with multiple faults and two faulty bearings.     

利用声信号对滚动轴承进行故障诊断的研究

旋转机械在运行过程中产生的声信号包含了滚动轴承的运行状态信息,且可采用非接触式测量,本文应用它对滚动轴承进行故障诊断。基于morlet小波变换的包络分析对采集的声信号进行降噪及包络处理,然后变换到频域,提取出特征频率并经过转换后作为线性神经网路的输入向量,辨识滚动轴承的状态。实验表明,本方法对滚动轴承故障诊断是有效的。     

Maximum cross-correlated kurtosis-based unsaturated stochastic resonance and its application to bearing fault diagnosis

Considering the random impulses of mechanical noise and the limitations involved while identifying mechanical fault impulse signals via traditional measurement indices of signal-to-noise ratio, which require the characteristic frequency to be known in advance, this study proposes an adaptive unsaturated stochastic resonance method employing maximum cross-correlated kurtosis as the signal detection index. The proposed method combines the features of a cross-correlated coefficient to indicate periodic fault transients and those of spectrum kurtosis to locate these transients in the frequency domain. Actual vibration signals collected from motor and gear bearings subjected to heavy noise are used to demonstrate the effectiveness of the proposed method. Through a coarse tree-based machine learning method, the proposed method is verified to be more suitable for explaining the periodic impulse components of bearing signals, as compared to the ensemble empirical mode decomposition denoising method and unsaturated stochastic resonance using the kurtosis-intercorrelation index.     

Continuous-scale mathematical morphology-based optimal scale band demodulation of impulsive feature for bearing defect diagnosis

Integer-scale structuring element is usually used in the traditional mathematical morphology (MM) for signal processing. When applied for impulsive feature demodulation of vibration signal of rolling element bearings, the integer-scale MM (ISMM) may lead to low resolution result and thus undermines its defect diagnosis capability. For this reason, this paper proposes a continuous-scale MM (CSMM) scheme by interpolation and re-sampling to improve scale resolution for more reliable fault signature extraction. Based on the frequency domain kurtosis criterion, a narrowband merging operation is employed to locate the optimal scale band that best reflects the impulsive feature from the CSMM analysis results. The demodulated components in the optimal scale band are employed to detect the existence of the bearing fault. The proposed optimal CSMM demodulation technique is evaluated using both simulated and experimental bearing vibration signals. The results show that, the CSMM is capable of generating demodulation signals with higher resolution, and the optimal scale band demodulation based on the CSMM can reliably extract impulsive features for bearing defect diagnosis.     

仿生鳍条姿态测量及其误差分析

在复杂环境下齿轮箱信号往往会淹没在噪声信号中,特征向量难以提取。为了有效的进行故障诊断,提出了基于最大相关反褶积(MCKD)总体平均经验模态分解(EEMD)近似熵和双子支持向量机(TWSVM)的齿轮箱故障诊断方法。首先采用MCKD方法对强噪声信号进行滤波处理,在采用EEMD方法对齿轮箱信号进行分解,分解后得到本征模函数(IMF)分量进行近似熵求解,得到齿轮特征向量,最后将其输入到TWSVM分类器中进行故障识别。仿真实验表明,采用MCKD-EEMD方法能够有效的提取原始信号,与其他分类器相比,TWSVM的计算时间短,分类效果好等优点。     

基于MCKD-EEMD近似熵和TWSVM的齿轮箱故障诊断

在复杂环境下齿轮箱信号往往会淹没在噪声信号中,特征向量难以提取;为了有效地进行故障诊断,提出了基于最大相关反褶积(MCKD)总体平均经验模态分解(EEMD)近似熵和双子支持向量机(TWSVM)的齿轮箱故障诊断方法;首先采用MCKD方法对强噪声信号进行滤波处理,在采用EEMD方法对齿轮箱信号进行分解,分解后得到本征模函数(IMF)分量进行近似熵求解,得到齿轮特征向量,最后将其输入到TWSVM分类器中进行故障识别;仿真实验表明,采用MCKD-EEMD方法能够有效地提取原始信号,与其他分类器相比,TWSVM的计算时间短,分类效果好等优点。     

A wavelet approach to fault diagnosis of a gearbox under varying load conditions

Varying load can cause changes in a measured gearbox vibration signal. However, conventional techniques for fault diagnosis are based on the assumption that changes in vibration signal are only caused by deterioration of the gearbox. There is a need to develop a technique to provide accurate state indicator of gearbox under fluctuating load conditions. This paper presents an approach to gear fault diagnosis based on complex Morlet continuous wavelet transform under this condition. Gear motion residual signal, which represents the departure of time synchronously averaged signal from the average tooth-meshing vibration, is analyzed as source data due to its lower sensitiveness to the alternating load condition. A fault growth parameter based on the amplitude of wavelet transform is proposed to evaluate gear fault advancement quantitatively. We found that this parameter is insensitive to varying load and can correctly indicate early gear fault. For a comparison, the advantages and disadvantages of other measures such as kurtosis, mean, variance, form factor and crest factor, both of residual signal and mean amplitude of continuous wavelet transform waveform, are also discussed. The effectiveness of the proposed fault indicator is demonstrated using a full lifetime vibration data history obtained under sinusoidal varying load.     

基于功率谱包络能量和SVM的舰用发动机故障诊断

发动机是军舰上的重要部件之一,其稳定性对军舰的正常航行具有重要影响。以舰用发动机关键部件(主泵轴承)为具体研究对象,提出了基于功率谱包络能量和支持向量机相结合的故障诊断方法。首先获取了大量可表征舰用发动机主泵轴承健康状态的振动加速度信息,对其进行功率谱分析,获得其功率谱的包络能量;以获取的舰用发动机主泵轴承功率谱的包络能量构建特征向量,并设计基于SVM的舰用发动机主泵轴承故障诊断模型,对主泵轴承的故障进行诊断研究。研究结果表明,采用基于功率谱包络能量和SVM相结合的舰用发动机关键部件故障诊断方法,可以很好实现主泵轴承的故障诊断效能,为舰用发动机主泵轴承故障诊断的工程应用奠定了基础。     

甲烷爆炸感应期内火焰光谱特征分析方法研究

基于目标发射光谱分析法的原理,通过分析小尺寸实验平台上测得的体积分数10%的甲烷爆炸火焰光谱数据,提出甲烷爆炸火焰光谱特征分析方法,包括频域特征参数光谱密度、波段辐射光强度、波段平均及偏差,时域特征参数波段辐射能量、时间段平均及偏差和特性参数偏度、峰度、半宽的计算方法;分析得出当甲烷爆炸火焰光谱波长值为某些定值时,光谱密度在1 nm范围内在正向与负向之间转换,表明光强密集程度变化剧烈;甲烷爆炸火焰光谱波段定积分在550～900 nm波段最强;甲烷爆炸火焰光谱信号随着波长的增大可测时间增长,信号强度在达到峰值后整体呈衰减趋势,在衰减过程中间隔出现依次减弱的强度增强;研究结果表明目标发射光谱分析法可应用于甲烷爆炸感应期内火焰光谱的动态半定量分析,分析得出的光谱特征可作为检测甲烷爆炸火焰的判据。     

A new blind fault component separation algorithm for a single-channel mechanical signal mixture

A vibration signal collected from a complex machine consists of multiple vibration components, which are system responses excited by several sources. This paper reports a new blind component separation (BCS) method for extracting different mechanical fault features. By applying the proposed method, a single-channel mixed signal can be decomposed into two parts: the periodic and transient subsets. The periodic subset is related to the imbalance, misalignment and eccentricity of a machine. The transient subset refers to abnormal impulsive phenomena, such as those caused by localized bearing faults. The proposed method includes two individual strategies to deal with these different characteristics. The first extracts the sub-Gaussian periodic signal by minimizing the kurtosis of the equalized signals. The second detects the super-Gaussian transient signal by minimizing the smoothness index of the equalized signals. Here, the equalized signals are derived by an eigenvector algorithm that is a successful solution to the blind equalization problem. To reduce the computing time needed to select the equalizer length, a simple optimization method is introduced to minimize the kurtosis and smoothness index, respectively. Finally, simulated multiple-fault signals and a real multiple-fault signal collected from an industrial machine are used to validate the proposed method. The results show that the proposed method is able to effectively decompose the multiple-fault vibration mixture into periodic components and random non-stationary transient components. In addition, the equalizer length can be intelligently determined using the proposed method.     

Cyclic statistics in rolling bearing diagnosis

Mechanical signals with amplitude modulated are characterized by periodic time-varying ensemble statistics and can be considered as cyclostationary. In this paper, the second order cyclic statistics, i.e. cyclic autocorrelation and cyclic spectrum, are introduced. A method of demodulation based on cyclic autocorrelation is derived from a signal model. The modulators and carrier are exhibited, respectively, in low- and high-frequency band of cyclic frequency domain. The three-dimensional spectral correlation figure, which represents cyclic frequency, frequency and spectral correlation strength simultaneously, is developed to express the demodulation results clearly. The method is tested by simulation signal and applied to diagnose rolling bearing faults. It obtained more information than other conventional methods, such as the frequency domain and the envelop detection. Furthermore, its effect is demonstrated by comparing with the wavelet envelope demodulation.     

基于速度平滑距离的高精度测量方法研究

随着卫星定位以及测定轨精度的提高，需要研究厘米量级的高精度测距系统。针对现有测控系统测量距离值存在较大随机误差的问题，提出了一种基于速度对距离值进行平滑的方法，以减小测距随机误差。为了保证速度的解算精度，给出了一种基于三阶锁相环路的跟踪接收方案，并对环路跟踪精度、速度平滑时间选择及优化等进行分析和仿真。仿真结果表明，新提出的接收方案和速率平滑距离方法能够实现对不同动态目标信号的有效跟踪，有效降低距离测量的随机误差，可为高精度航天测控系统提供一种解决思路。     

Ion-focused propagation of a relativistic electron beam in the self-generated plasma in atmosphere

It is known that ion-focused regime (IFR) can effectively suppress expansion of a relativistic electron beam (REB). Using the particle-in-cell Monte Carlo collision (PIC-MCC) method, we numerically investigate the propagation of an REB in neutral gas. The results demonstrate that the beam body is charge neutralization and a stable IFR can be established. As a result, the beam transverse dimensions and longitudinal velocities keep close to the initial parameters. We also calculate the charge and current neutralization factors of the REB. Combined with envelope equations, we obtain the variations of beam envelopes, which agree well with the PIC simulations. However, both the energy loss and instabilities of the REB may lead to a low transport efficiency during long-range propagation. It is proved that decreasing the initial pulse length of the REB can avoid the influence of electron avalanche. Using parts of REB pulses to build a long-distance IFR in advance can improve the beam quality of subsequent pulses. Further, a long-distance IFR may contribute to the implementation of long-range propagation of the REB in space environment.     

Exchanged ridge demodulation of time-scale manifold for enhanced fault diagnosis of rotating machinery

The vibration or acoustic signal from rotating machinery with localized fault usually behaves as the form of amplitude modulation (AM) and/or frequency modulation (FM). The demodulation techniques are conventional ways to reveal the fault characteristics from the analyzed signals. One of these techniques is the time-scale manifold (TSM) ridge demodulation method with the merits of good time–frequency localization and in-band noise suppression properties. However, due to the essential attribute of wavelet ridge, the survived in-band noise on the achieved TSM will still disturb the envelope extraction of fault-induced impulses. This paper presents an improved TSM ridge demodulation method, called exchanged ridge demodulation of TSM, by combining the benefits of the first two TSMs: the noise suppression of the first TSM and the noise separation of the second TSM. Specifically, the ridge on the second TSM can capture the fault-induced impulses precisely while avoiding the in-band noise smartly. By putting this ridge on the first TSM, the corresponding instantaneous amplitude (IA) waveform can represent the real envelope of pure faulty impulses. Moreover, an adaptive selection method for Morlet wavelet parameters is also proposed based on the smoothness index (SI) in the time-scale domain for an optimal time-scale representation of analyzed signal. The effectiveness of the proposed method is verified by means of a simulation study and applications to diagnosis of bearing defects and gear fault.     

Complex signal analysis for wind turbine planetary gearbox fault diagnosis via iterative atomic decomposition thresholding

The vibration signals from complex structures such as wind turbine (WT) planetary gearboxes are intricate. Reliable analysis of such signals is the key to success in fault detection and diagnosis for complex structures. The recently proposed iterative atomic decomposition thresholding (IADT) method has shown to be effective in extracting true constituent components of complicated signals and in suppressing background noise interferences. In this study, such properties of the IADT are exploited to analyze and extract the target signal components from complex signals with a focus on WT planetary gearboxes under constant running conditions. Fault diagnosis for WT planetary gearboxes has been a very important yet challenging issue due to their harsh working conditions and complex structures. Planetary gearbox fault diagnosis relies on detecting the presence of gear characteristic frequencies or monitoring their magnitude changes. However, a planetary gearbox vibration signal is a mixture of multiple complex components due to the unique structure, complex kinetics and background noise. As such, the IADT is applied to enhance the gear characteristic frequencies of interest, and thereby diagnose gear faults. Considering the spectral properties of planetary gearbox vibration signals, we propose to use Fourier dictionary in the IADT so as to match the harmonic waves in frequency domain and pinpoint the gear fault characteristic frequency. To reduce computing time and better target at more relevant signal components, we also suggest a criterion to estimate the number of sparse components to be used by the IADT. The performance of the proposed approach in planetary gearbox fault diagnosis has been evaluated through analyzing the numerically simulated, lab experimental and on-site collected signals. The results show that both localized and distributed gear faults, both the sun and planet gear faults, can be diagnosed successfully.     

基于伪相关函数的多级电平编码符号信号通用无模糊跟踪方法

针对新一代全球导航卫星系统(GNSS)中多级电平编码符号(MCS)信号存在的跟踪模糊问题,本文提出了一种通用的MCS信号无模糊跟踪方法.首先推导了不同MCS信号互相关函数的统一表达式,并给出了伪相关函数的定义;然后深入分析了实现无模糊跟踪需要满足的约束条件,推导了两路参考信号的通用构造方法以及相互之间的关系,为具体MCS信号的求解提供了极大的便利;进而给出了利用本文方法的GNSS接收机码跟踪环路模型.作为MCS信号的特例,分别讨论了本文方法在四种二进制偏移载波信号跟踪中的应用.仿真结果表明,本文方法能够有效解决MCS信号的跟踪模糊问题,具有良好的性能和广阔的应用前景.