最优共振频带提取的高速列车轴承故障诊断

在非高斯噪声与周期振动信号的干扰下,高速列车轴承的故障特征提取较为困难,针对这一问题,提出了一种新的最优故障频带的判别方法,并通过经验小波变换（Empirical Wavelet Transform,EWT）对故障频带进行提取,从而实现高速列车轴承故障的有效诊断。该方法首先提供了完整的频域分割框架,得到不同中心频率、不同带宽的频带分布;为了得到各频带所包含的故障信息含量,提出了新的故障特征指标HSIB,根据HSIB的变化趋势识别最优频带;最后进行经验小波变换,将选取的故障频带通过正交滤波器组,对得到的分量信号进行Hilbert变换,得到轴承的故障特征频率。通过仿真和实验数据验证,选取的最优频带包含了丰富的故障信息,可以准确地提取出轴承故障特征频率的基频和倍频成分,有效确定轴承故障。     

基于双树复小波包变换的钢丝绳断丝损伤信号特征信息提取研究北大核心CSCD

针对钢丝绳断丝损伤信号特征信息难以有效提取的问题,提出一种基于双树复小波包变换与奇异值分解相结合的时频域特征信息提取方法。首先将钢丝绳断丝损伤信号采用双树复小波包变换为等长的频带子信号,构造时频域空间状态矩阵,然后采用奇异值方法提取各频带子信号的奇异值,组成表征各类损伤状态的特征向量,得到钢丝绳断丝损伤信号的特征信息矩阵。采用距离可分离性判据与传统时域特征信息提取方法相比较,结果表明双数复小波包变换与奇异值分解相结合的特征信息提取方法具有更强的分类能力。     

低信噪比下的滚动轴承早期微弱故障识别

低信噪比下的滚动轴承早期微弱故障识别是轴承故障诊断领域的一个难点问题，基于希尔伯特变换解调的包络谱分析法虽是得到广泛工程应用的轴承故障检测经典方法，但其对信噪比过低的轴承早期微弱故障诊断能力不足。针对这一问题，采用时频域高阶统计量——谱峭度开展滚动轴承早期微弱故障识别研究。利用滚动轴承从完好逐渐发展到外圈损伤失效的全寿命周期试验数据进行分析，结果表明：对于轴承出现的早期微弱故障，谱峭度法能够通过识别提取位于高信噪比共振频带的微弱故障信号，实现轴承早期微弱故障识别，相比直接采用包络谱分析法提前了200 min检测出微弱故障。     

基于改进双树复小波变换的轴承多故障诊断

针对双树复小波变换产生频率混叠的缺陷,提出了改进双树复小波变换的轴承多故障诊断方法,该方法综合利用了双树复小波包变换和经验模态分解技术。首先,利用双树复小波变换将振动信号分解成不同频带的分量;然后,将各小波分量进行经验模态分解,获得各小波分量的主频率分量信号;最后,计算各小波分量的主频率分量信号的包络谱,根据包络谱识别齿轮箱轴承的故障部位和类型。通过仿真信号和齿轮箱轴承多故障振动实验信号的研究结果表明,该方法不仅消除了频率混叠现象,提高了信噪比和频带选择的正确性,而且提高了从强噪声环境中提取瞬态冲击特征的能力,能有效识别轴承的故障类型。     

利用奇异值分解的信号降噪方法

为了提高测试信号的信噪比,针对奇异值分解降噪法中有效秩阶次的选择以及重构矩阵结构的确定两个关键问题,提出了一种基于信号频率成分的奇异值降噪方法.该方法利用信号快速傅里叶变换结果中主频率个数来确定有效秩阶次,通过降噪信号的信噪比和均方差大小确定重构矩阵结构,并采用不同频率成分的几组信号对该方法进行了验证.结果表明,有效秩的阶次是源信号主频个数的2倍,并且这种倍数关系不随重构矩阵行列数的变化而变化;在工程应用中,重构矩阵的最佳行数取信号数据长度的一半,可以得到较好的降噪效果;除傅里叶变换结果中有用信号频率与噪声频率难以区分的情形外,无论是白噪声还是色噪声,该方法都十分有效.     

RWESOS-VPMCD方法对超声缺陷信号的识别研究

在通过特征值间的内在关系建立预测模型的变量预测模式识别方法(VPMCD)中,传统判别方法受特征向量中的个别特征预测异常值影响大,易导致分类错误.提出基于比值加权的最小误差平方和的判别函数(RWESOS),可将异常预测的特征权重大幅降低,提升正确预测特征的权重,从而提高分类准确率.实验表明,在对不同缺陷大小的超声检测信号的识别中,使用RWESOS判别函数的RWESOS-VPMCD方法的识别率比BP神经网络和普通判别函数的VPMCD方法的识别率分别提高了4％和11％.     

积分补偿随机共振在盾构机轴承故障检测中的应用

针对盾构机轴承早期故障微弱信号难以检测识别以及小参数随机共振系统难以检测大参数输入信号的问题,提出了一种积分补偿法调节大参数以实现随机共振。首先利用积分补偿的方式将输入大频率信号频率乘以二倍圆周率得到补偿系数;其次对小频率参数系统进行相应的积分补偿,即对模型中的非线性方程进行变换并乘以补偿系数,抵消阻尼对输入信号的衰减作用,并由积分补偿方程建立新的非线性共振系统模型;最后将高频信号输入新建立的系统模型产生随机共振动态响应,并对其进行FFT变换,获得输出信号的故障信息。由此通过对振动加速度传感器采集的轴承径向振动信号分析,可以有效获得轴承故障特征频率,仿真与实验验证了理论方法的正确性。     

基于局部平衡判别投影的旋转机械故障诊断方法

针对旋转机械故障数据的多类别、高维复杂特性导致的分类困难问题,提出一种基于局部平衡判别投影(LBDP)的故障数据集降维方法。从时域、频域和时频域多个角度提取转子振动信号的混合特征,构建原始高维故障特征集;通过LBDP选择出其中最能反映故障本质的敏感特征子集;将得到的低维特征子集输入到K近邻分类器(KNN)中进行故障模式辨识。通过一个双转子系统的振动信号集合验证了所提出方法的有效性,证明了该方法能够全面地提取出局部判别信息,使故障类别之间的差异性更清晰。     

高光谱遥感影像地面伪装目标检测方法的研究

根据光谱揭露伪装的检测机制,对目前国内外的许多绿色伪装材料和多种绿色植被背景的光谱特性进行了分析,探讨了实验目标光谱模拟伪装材料的检测技术.通过光谱特征选择及空间降维处理,建立了判别函数,确定了判别规则.寻找了适合区分人工目标与背景光谱的最佳分类特征和判别函数.     

一种强噪声环境下的水声信号提取算法研究（英文）

在对水中目标进行探潜、跟踪、定位时,需要对目标辐射的水声信号进行频域分析、相关性计算等处理。在强噪声环境下,目标的信号可能会被噪声淹没,导致无法有效识别目标。针对这一问题,本文提出利用傅里叶降噪预处理与小波变换相结合进行信噪分离,实现强噪声环境下的水声信号提取。设计了傅里叶系数阈值调整与小波阈值变换相结合的组合算法,并对算法的性能进行了测试。仿真实验表明,在信噪比为-15 dB时,该组合算法能够实现水声信号的有效提取,并将信噪比提高到了8.2 dB。     

基于Morlet小波改善飞行颤振试验参数识别精度的方法

为了解决激励能量有限和现场测试数据量较少、噪声大，系统参数识别的准确度差的问题，采用Morlet小波时频滤波和频域参数识别相结合的方法进行参数识别来提高精度。基于Morlet小波函数建立特性滤波器组进行时频域滤波，讨论滤波参数的选取方法，采用有理正交多项式（RFOP）拟合算法进行频域参数识别，基于欧洲航空界广泛采用的GARTEUR飞机模型数据建立密频模态系统，进行飞行颤振的试验数据仿真。结果表明该方法在信号噪声较大时，可以有效地提高系统参数识别的精度。     

内燃机进气压力的小波分析

探讨了进气压力信号与气阀机构状态之间的关系，对进气压力信号进行二进小波分解，利用分解后尺度1信号的频域特征对气阀机构进行状态分类与识别。结果与实例分析表明方法是有效和可行的。     

Development of a system for monitoring tool condition using acousto-optic emission signal in face turning—an experimental approach

In automated manufacturing systems, one of the most important issues is accurate detection of the tool conditions under given cutting conditions so that worn tools can be identified and replaced in time. In metal cutting as a result of the cutting motion, the surface of workpiece will be influenced by cutting parameters, cutting force, and vibrations, etc. But the effects of vibrations have been paid less attention. In the present paper, an investigation is presented of a tool condition monitoring system, which consists of a fast Fourier transform preprocessor for generating features from an online acousto-optic emission (AOE) signals to develop a database for appropriate decisions. A fast Fourier transform (FFT) can decompose AOE signals into different frequency bands in the time domain. Present work uses a laser Doppler vibrometer for online data acquisition and a high-speed FFT analyser used to process the AOE signals. The generation of the AOE signals directly in the cutting zone makes them very sensitive to changes in the cutting process due to vibrations. AOE techniques is a relatively recent entry into the field of tool condition monitoring. This method has also been widely used in the field of metal cutting to detect process changes like displacement due to vibration and tool wear, etc. In this research work the results obtained from the analysis of acousto-optic emission sensor employs to predict flank wear in turning of AISI 1040 steel of 150 BHN hardness using Carbide insert and HSS tools. The correlation between the tool wear and AOE parameters is analyzed using the experimental study conducted in 16 H.P. all geared lathe. The encouraging results of the work pave the way for the development of a real-time, low-cost, and reliable tool condition monitoring system. A high degree of correlation is established between the results of the AOE signal and experimental results in identification of tool wear state.     

基于多尺度信息熵特征和RBF神经网络的气液两相流流型识别方法

根据小波包变换能将信号按任意时频分辨率分解到不同频段的特性，提出一种基于小波包多尺度信息熵的流型识别方法。该方法首先对采集到的压差波动信号进行4层小波包分解，在通频范围内得到分布在不同频段内的分解信号，进而建立流型的多尺度信息熵特征向量。并以此特征向量作流型样本对RBF神经网络进行训练，实现流型的智能化识别。试验结果表明，训练成功的RBF网络能很好地识别水平管内的4种流型，为流型识别开辟了一条新的途径。     

金属材料小缺陷超声反射信号建模及识别

为了能从含噪声金属材料超声检测信号中有效识别出微小缺陷回波,建立了金属材料超声反射信号模型并提出了基于相关系数的微小缺陷回波识别方法。对含微小缺陷金属材料超声脉冲反射信号的成分进行分析,建立了基于散射声场与高斯回波理论的优化超声回波模型。设计了超声缺陷回波位置识别方法。该方法对超声脉冲反射信号去噪后,取探头发射脉冲信号为参考信号;然后与去噪后的信号逐段求解相关系数;最后对该相关系数序列进行阈值化处理,获得缺陷回波在超声回波信号中的位置。将利用上述优化超声回波模型生成的超声反射信号及其频谱与实验获得的金属材料超声反射信号及其频谱进行了对比,结果表明:两者的时频域特征具有一致性。当将阈值设定为相关系数序列最大值的60%时,能够有效从超声背散射信号中识别出金属材料微小缺陷回波。     

基于快速谱峭度图的EEMD内禀模态分量选取方法

针对在总体平均经验模式分解(ensemble empirical mode decomposition,简称EEMD)的多个内禀模态分量(intrinsic mode function,简称IMF)中,如何选取出反应故障特征的敏感IMF的问题,提出一种基于快速谱峭度图的敏感IMF选取方法。由EEMD分解获得的一组无模式混淆的IMF,计算原信号及各个IMF的快速谱峭度图,选择每个快速谱峭度图中谱峭度最大值所处的频带作为参考频带,比较各个IMF的参考频带与原信号谱峭度最大值所处频带之间的从属关系,筛选出反应故障特征的敏感IMF,为后续故障诊断提供特征信息。将该方法应用于模拟仿真信号及滚动轴承滚动体故障信号,验证了方法的有效性。     

基于GA-SVM算法的通信网络入侵信号自动识别技术

针对当前通信系统入侵行为自动识别技术存在入侵信号样本识别成功率较低、误识别率和漏识别率较高的问题,提出基于 GA-SVM 算法的通信网络入侵信号自动识别技术。利用混沌原理提取通信网络入侵的非平稳信号时域特征,并凭借自回归模型提取对应频域特征,捕捉邻域入侵信号间的非线性时空动作频率,评价相邻行为间的状态关联性,预测入侵信号后续行为,完成入侵信号的识别。实验表明,所提方法识别精度高、误识别率较低,漏识别率非常低,具有可应用于实际的理论价值。     

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.     

Prediction of metal deposition from arc sound and weld temperature signatures in pulsed MIG welding

The weld deposition efficiency is an important economic factor like productivity and weld quality in gas metal arc welding (GMAW). There is a strong relationship between arc sound signals and arc stability (or deposition efficiency) in GMAW. In this work, the variation of weld deposition efficiency with various pulse parameters in pulsed metal inert gas welding was investigated. The arc sound signal along with current and voltage signals were acquired and analyzed in time domain as well as in frequency domain. The sound signal kurtosis and arc power were found to be highly correlated with welding process stability. The weld deposition efficiency was also related to weld surface peak temperature. Finally, an attempt was made to correlate the sound time domain as well as frequency domain features of sound signal with the deposition efficiency. The variation of pulse shape with the duty factor also influenced the deposition efficiency as evidenced by in fast Fourier transform analysis.     

Online welding quality monitoring based on feature extraction of arc voltage signal

Arc sensing plays a significant role in the control and monitoring of welding quality for aluminum alloy pulsed gas touch argon welding (GTAW). A method for online quality monitoring was proposed based on the analysis of acquired arc voltage signal, through which two algorithms of feature extraction were developed in time and frequency domain, respectively. In time domain,the wavelet packet transform was carried out to eliminate the pulse interference of the feature parameter curve. In frequency domain, the other new algorithm was proposed based on the voltage power spectrum density (PSD) which was calculated by using the improved Welch algorithm and divided into five frequency bands before the statistic parameters were extracted. The correlation between the feature parameters in different frequency bands and welding defects were carefully analyzed to select a more sensitive one as the monitoring parameters. The proposed algorithms on this paper were verified to be capable of detecting lack of penetration, burn through, and the defect caused by lack of gas.