Research on Feature Extraction Method for Low-Speed Reciprocating Bearings Based on Segmented Short Signal Modulation Signal Bispectrum Slicing

Bearing condition monitoring and fault diagnosis (CMFD) can investigate bearing faults in the early stages, preventing the subsequent impacts of machine bearing failures effectively. CMFD for low-speed, non-continuous operation bearings, such as yaw bearings and pitch bearings in wind turbines, and rotating support bearings in space launch towers, presents more challenges compared to continuous rolling bearings. Firstly, these bearings have very slow speeds, resulting in weak collected fault signals that are heavily masked by severe noise interference. Secondly, their limited rotational angles during operation lead to a restricted number of fault signals. Lastly, the interference from deceleration and direction-changing impact signals significantly affects fault impact signals. To address these challenges, this paper proposes a method for extracting fault features in low-speed reciprocating bearings based on short signal segmentation and modulation signal bispectrum (MSB) slicing. This method initially separates short signals corresponding to individual cycles from the vibration signals based on encoder signals. Subsequently, MSB analysis is performed on each short signal to generate MSB carrier-slice spectra. The optimal carrier frequency and its corresponding modulation signal slice spectrum are determined based on the carrier-slice spectra. Finally, the MSB modulation signal slice spectra of the short signal set are averaged to obtain the overall average feature of the sliced spectra.

GEAR CRACK EARLY DIAGNOSIS USING BISPECTRUM DIAGONAL SLICE

A study of bispectral analysis in gearbox condition monitoring is presented.The theory of bispectrum and quadratic phase coupling (QPC) is first introduced, and then equationsfor computing bispectrum slices are obtained. To meet the needs of online monitoring, a simplifiedmethod of computing bispectrum diagonal slice is adopted. Industrial gearbox vibration signalsmeasured from normal and tooth cracked conditions are analyzed using the above method. Experimentsresults indicate that bispectrum can effectively suppress the additive Gaussian noise andchracterize the QPC phenomenon. It is also shown that the 1-D bispectrum diagonal slice can capturethe non-Gaussian and nonlinear feature of gearbox vibration when crack occurred, hence, this methodcan be employed to gearbox real time monitoring and early diagnosis.

Fault Detection and Diagnosis of a Gearbox in Marine Propulsion Systems Using Bispectrum Analysis and Artificial Neural Networks

A marine propulsion system is a very complicated system composed of many mechanical components.As a result,the vibration signal of a gearbox in the system is strongly coupled with the vibration signatures of other components including a diesel engine and main shaft.It is therefore imperative to assess the coupling effect on diagnostic reliability in the process of gear fault diagnosis.For this reason,a fault detection and diagnosis method based on bispectrum analysis and artificial neural networks (ANNs) was proposed for the gearbox with consideration given to the impact of the other components in marine propulsion systems.To monitor the gear conditions,the bispectrum analysis was first employed to detect gear faults.The amplitude-frequency plots containing gear characteristic signals were then attained based on the bispectrum technique,which could be regarded as an index actualizing forepart gear faults diagnosis.Both the back propagation neural network (BPNN) and the radial-basis function neural network (RBFNN) were applied to identify the states of the gearbox.The numeric and experimental test results show the bispectral patterns of varying gear fault severities are different so that distinct fault features of the vibrant signal of a marine gearbox can be extracted effectively using the bispectrum,and the ANN classification method has achieved high detection accuracy.Hence,the proposed diagnostic techniques have the capability of diagnosing marine gear faults in the earlier phases,and thus have application importance.

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.

A Study of Motor Bearing Fault Diagnosis using Modulation Signal Bispectrum Analysis of Motor Current Signals

Failure of induction motors are a large concern due to its influence over industrial production. Motor current signature analysis (MCSA) is common practice in industry to find motor faults. This paper presents a new approach to detection and diagnosis of motor bearing faults based on induction motor stator current analysis. Tests were performed with three bearing conditions: baseline, outer race fault and inner race fault. Because the signals associated with faults produce small modulations to supply component and high nose levels, a modulation signal bispectrum (MSB) is used in this paper to detect and diagnose different motor bearing defects. The results show that bearing faults can induced a detestable amplitude increases at its characteristic frequencies. MSB peaks show a clear difference at these frequencies whereas conventional power spectrum provides change evidences only at some of the frequencies. This shows that MSB has a better and reliable performance in extract small changes from the faulty bearing for fault detection and diagnosis. In addition, the study also show that current signals from motors with variable frequency drive controller have too much noise and it is unlikely to discriminate the small bearing fault component.

UWB radar target recognition based on time-domain bispectrum

Complex targets are irradiated by UWB radar, not only the mirror scattering echoes but also the multiscattering interacting echoes are included in target echoes. These two echoes can not be distinguished by classical frequency spectrum and power spectrurm. Time-domain bispectrum features of UWB radar signals that mingled with noise are analyzed, then processing this kind of signal using the method of time-domain bispectrum is experimented. At last, some UW-B radar returns with different signal noise ratio are simulated using the method of time-domain bispectrum Theoretical analysis and the results of simulation show that the method of extraction partial features of UWB radar targets based on time-domain bispectrum is good, and target classification and recognition can be implemented using those features.

Bispectrum Analysis in Fault Diagnosis of Gears

The application ofbispectrum analysis in fault diagnosis o f gears is studied in this paper. Bispectrum analysis is capable of removing Gau ssian or symmetric non-Gaussian noise and providing more information than power spectrum analysis.The results of the research show that normal gear sig nals, cracked gear signals and broken gear signals can be easily distinguished b y using bispectrumas the signal features. The bispectrum diagonal slice B_x(ω_1,ω_2) can be used to identifythe gear condition automatically.

Identification of Noisy Utterance Speech Signal using GA-Based Optimized 2D-MFCC Method and a Bispectrum Analysis

One-dimensional Mel-Frequency Cepstrum Coefficients (1D-MFCC) in conjunction with a power spectrum analysis method is usually used as a feature extraction in a speaker identification system. However, as this one dimensional feature extraction subsystem shows low recognition rate for identifying an utterance speech signal under harsh noise conditions, we have developed a speaker identification system based on two-dimensional Bispectrum data that was theoretically more robust to the addition of Gaussian noise. As the processing sequence of ID-MFCC method could not be directly used for processing the two-dimensional Bispectrum data, in this paper we proposed a 2D-MFCC method as an extension of the 1D-MFCC method and the optimization of the 2D filter design using Genetic Algorithms. By using the 2D-MFCC method with the Bispectrum analysis method as the feature extraction technique, we then used Hidden Markov Model as the pattern classifier. In this paper, we have experimentally shows our developed methods for identifying an utterance speech signal buried with various levels of noise. Experimental result shows that the 2D-MFCC method without GA optimization has a comparable high recognition rate with that of 1D-MFCC method for utterance signal without noise addition. However, when the utterance signal is buried with Gaussian noises, the developed 2D-MFCC shows higher recognition capability, especially, when the 2D-MFCC optimized by Genetics Algorithms is utilized.

傅里叶分解和调制信号双谱的滚动轴承故障诊断

在噪声干扰较强的环境下,为了克服傅里叶分解方法(Fourier Decomposition Method,FDM)在分析调制信号及单独使用调制信号双谱(Modulated Signal Bispectrum,MSB)在分析非平稳信号方面的不足,提出了一种FDM和MSB相结合的滚动轴承故障诊断方法。首先,使用FDM按照高频到低频的方式搜寻傅里叶固有模态函数分量(Fourier Intrinsic band Functions,FIBFs);以加权峭度指标作为评判标准,对信号进行重构,确保得到最佳的信号;然后对新的信号利用MSB分析方法进行解调处理,最终通过复合切片谱实现故障特征频率的提取。最后,通过上述方法对模拟信号和滚动轴承外圈故障信号进行分析,其研究结果表明:该方法能够有效地提取故障特征频率,并且与常规双谱进行对比,验证所提方法的优越性。

低速往复运转轴承故障的调制信号双谱切片总体平均特征提取

风电机组的偏航轴承和变桨轴承、航天发射塔架的回转支承轴承、起重机和挖掘机的转盘轴承等,都具有低速往复运转的特点。低速往复运转轴承的故障诊断极具挑战:低速工况下损伤接触的冲击力小,损伤冲击信号弱;减速换向冲击信号对故障冲击信号的干扰大;覆盖多个往复运转行程的长信号不具有周期性,等等。为了解决上述问题,提出一种基于调制信号双谱(Modulation Signal Bispectrum,MSB)切片总体平均的低速往复运转轴承故障诊断方法。首先,利用转速跟踪过零点对振动信号进行信号重采样处理,并依据编码器信号从重采样信号中分离出单个行程的短信号集合;然后,对每一个短信号进行MSB分析,生成MSB的载波切片谱,根据载波切片谱寻找最优载波频率及其对应的调制信号切片谱;最后,对短信号集合的MSB调制信号切片谱进行总体平均,生成切片谱总体平均特征。故障试验数据验证结果表明,MSB切片总体平均特征能够有效诊断低速往复运转轴承的故障。

基于增强积分双谱的轨道交通辐射源识别方法

[目的]城市轨道交通无线通信系统中存在大量外部干扰信号,对行车安全构成重大隐患。针对辐射源射频特征易受噪声与干扰影响,导致识别准确率低的问题,须提出一种基于增强对角积分双谱的通信辐射源个体识别方法,为轨道交通无线通信系统安全保障提供有效新途径。[方法]分析了对角相关局部积分双谱(DCLIB)的数据处理过程及原理,阐述了双谱变换的计算、增强对角积分双谱的计算、自适应双谱积分区间的划分,以及基于残差网络的辐射源识别方法。基于实际Wi-Fi(无线保真)设备进行仿真试验,对DCLIB方法和其他辐射源识别方法的识别效果进行分析对比。[结果及结论]DCLIB方法先估计通信辐射源信号的双谱,并利用次对角线各平行线的自相关特性形成新的谱信息以增强信号的细微特征;然后依据谱信号强度自适应选取合理的谱信号积分区间,在降低噪声影响的同时降低算法的计算复杂度,从而获得增强的对角积分双谱;进而将所提DCLIB信号作为辐射源的射频指纹特征,采用深度残差网络实现辐射源个体识别。基于实际Wi-Fi设备的仿真识别试验结果表明,DCLIB方法的识别准确率最优,并具有良好的抗噪声性能。

全相位FFT算法高精度谐波测量方法

针对电力系统精确测量谐波信号频谱特性这一技术问题,该文提出一种基于全相位FFT算法的高精度谐波测量方法。通过低漂移信号转换电路和程控滤波器对被测信号进行调理,再经高精度A/D转换器变换成数字量传至MCU进行全相位FFT变换,选择离散频谱中幅值最大的两根谱线进行校正得到信号精确测量结果。利用LTspice电路与Matlab算法协同仿真,结果表明:在输入信号为理想谐波、复杂谐波与间谐波、叠加白噪声信号的基波情况下,所提出的方法均可实现对信号频率和幅值的准确测量。在与传统FFT双谱线校正和加窗FFT双谱线校正相比,该文提出的测量方法精度更高、抗噪性更好,频谱泄漏抑制能力更强,适用于对电力信号的高精度测量。

基于积分双谱的通信辐射源个体识别方法

随着网络中无线设备制造工艺的提高,不同设备的指纹差别更加细微,且复杂环境中辐射源的射频指纹易受噪声与干扰的影响,进一步给辐射源个体识别增加了难度。如何提取更精细化的能够表征设备本质特征的射频指纹成为一个难点。针对该问题,提出了一种基于轴向积分双谱和矩形积分双谱指纹特征与融合分类器的通信辐射源个体识别方法。通过对识别同厂家同型号同批次的8个无线数传电台E90-DTU设备的实验测试,发现该方法在视距场景、视距场景与非视距场景的混合场景中都表现出良好的识别准确率。结果表明:基于积分双谱指纹特征的通信辐射源个体识别方法在不同场景下都具有较高的识别精度。该方法为解决通信辐射源个体识别问题提供了有效的解决方案,并具有广泛的应用前景。

基于双谱三维向量的雷达辐射源个体识别

基于相位噪声造成的辐射源个体差异,提出了一种新的雷达辐射源个体识别算法。该算法以由双谱熵、双谱能量熵以及双谱主成分均值所组成的三维向量为特征向量,以K-均值(K-means)算法为分类器,完成了雷达辐射源个体识别。相比于现在比较主流的使用深度学习模型作为分类器的辐射源个体识别算法,该算法无需繁琐的模型训练步骤也能取得较好的识别效果。仿真结果表明,在信噪比为0 dB时,该算法依然能确保80%的识别正确率,算法性能优越。

基于轴向积分双谱的假目标干扰特征提取与识别方法

针对间歇采样转发式干扰识别需求,提出了一种基于轴向积分双谱的假目标干扰特征提取和识别方法。该方法分为特征提取与干扰识别两个方面,首先对干扰和目标信号进行积分双谱特征提取,然后利用神经网络对特征进行分类,实现对假目标干扰的有效识别。仿真结果表明,采用该方法提取的目标信号和干扰信号特征差异明显,有利于提升后续的识别效果。

改进型EEMD和MSB解调方法及其在轴承故障特征提取中的应用

针对滚动轴承振动信号的强非线性和非平稳特性,提出了一种基于改进集成经验模态分解(IEEMD)和调制信号双谱(MSB)分析的故障特征提取方法。将集成经验模态分解(EEMD)应用于滚动轴承的振动信号处理,将其分解成一系列的本征模态函数(IMFs);通过累计均值(MSAM)准则将IMFs自适应地分为低频IMFs和高频IMFs,其中高频IMFs采用小波阈值降噪进行处理;将降噪后的高频IMFs与低频IMFs进行重构以获取高信噪比的瞬态脉冲信号;利用MSB进一步抑制瞬态脉冲信号中的随机噪声和干扰分量,并提取信号故障特征。与谱峭度(SK)和WEEMD-MSB分析结果进行对比,验证了该方法在轴承微弱故障特征提取方面的优越性。

基于伽玛通滤波器的双谱特征语音可懂度算法

针对现有的语音可懂度评价方法不能真实贴近人耳对语音的感知过程,提出一种基于人耳听觉特性的双谱特征预测语音可懂度评价(Gammatone-bspectral speech intelligibility metric, GBSIM)算法。充分利用双谱可以检测语音信号中的非线性相位耦合,抑制非高斯信号中的高斯噪声的特性,采用可以模拟人工耳蜗模型的Gammatone滤波器组,通过滤波处理将输入的语音信号分为32个听觉子频带,用三阶统计量对每个子频带的语音信号进行双谱估计并提取单一特征值来计算语音的可懂度。实例验证结果表明,该方法对信号失真变化敏感,其评价结果与主观评价具有很高的相关度,相对于传统的语音可懂度评价算法具有更好的评价效果。

注意力机制和CNN结合的雷达辐射源个体识别

针对雷达辐射源个体识别准确率低、抗噪性能差和训练时间长的问题,提出了一种注意力机制和卷积神经网络相结合的方法。首先根据雷达发射机功率放大器的硬件差异,建立雷达辐射源的系统模型;其次,对雷达信号进行双谱分析,得到的双谱图作为网络输入;然后,将注意力机制引入优化后的卷积神经网络,提高对个体特征的学习能力;最后,与现有方法对比,验证算法的有效性。实验证明,相比卷积神经网络,所提方法识别准确率提高5%,训练时间缩短一半。

基于CEEMDAN降噪与双谱分析的滚动轴承故障诊断

滚动轴承早期故障信号中的噪声成分会影响到故障特征的提取。为了提高含噪故障信号中滚动轴承早期故障特征提取的准确性,将基于自适应噪声的完备经验模态分解(CEEMDAN)用于滚动轴承振动信号的降噪中,并对降噪后的轴承故障信号进行双谱分析。结果表明:CEEMDAN可有效去除轴承振动信号中的低频噪声干扰,经CEEMDAN降噪后的不同轴承故障信号的双谱全局图存在明显差异,根据这些差异可在宏观上对不同轴承故障加以区分;通过经CEEMDAN降噪后的不同轴承故障信号的双谱细节图可以正确提取不同轴承故障的特征频率,从而实现对各轴承故障的有效诊断。CEEMDAN降噪结合双谱分析可为滚动轴承故障诊断提供一种新的有效方法。

融合双谱特征的雷达辐射源个体识别方法

双谱以其独特的抑制高斯有色噪声的优势,被广泛应用于雷达辐射源识别分析。采用双谱对角线作为雷达辐射源识别特征可以很大程度上减少计算量,但是使用双谱对角线作为唯一识别特征,会损失信号的部分幅度和分布特征。针对上述问题,提出了1种基于辐射源信号双谱提取二次特征的方法。取双谱作为二次特征提取对象,根据信息熵概念定义双谱奇异值全局熵和双谱对角线的幅度分散熵,联合双谱对角线及其积分结果作为特征向量,将其送入神经网络进行识别。实验表明,相比双谱对角线法,该方法对辐射源识别的正确率平均提高约3.3%。