一种改进时域同步平均的滚动轴承故障诊断方法研究

复杂结构设备的滚动轴承其振动信号成分复杂,故障信号微弱,信噪比很小,常规诊断方法难以有效消除背景噪声,有效提取故障信息.文中研究了基于相位补偿时域同步平均的滚动轴承故障诊断方法,并根据轴承故障信号存在调制的特点,探讨了同步周期的合理选取.通过对轴承内圈故障的仿真研究,验证了相位补偿时域同步平均方法的有效性,它能够同时提取故障特征频率与调制频率,为精确诊断轴承故障提供了新的途径.经进一步实验研究,证明该方法是有效的.     

基于联合时频分析的设备状态评估方法

在机电设备状态评估过程中,提出了一种基于联合时频分析的振动、噪声信号测量与分析方法.该方法可以通过综合利用机电设备运行过程中的振动与噪声信号中的有用信息,完成对整机运行状态评估.最后,以某舰船的电机作为实验对象,构建一套振动和噪声测量系统,并运用戈勃变换对所测得的振动和噪声信号进行联合时频分析,从而得到电机真实的运行状态.实验结果表明所提出的测量与分析方法是正确有效的.     

基于小波变换的高速列车表面脉动压力提取

高速列车表面压力测试过程中,为了克服微型压阻式气压传感器测试的脉动压力信噪比低的缺点,有效提取出脉动压力,建立了传感器输出模型;利用相关系数法确定分解层数,根据3原则计算分层阈值,提出一种小波变换阈值去噪方法.用该方法对CRH(China railway high-speed)某型动车组静态测试信号进行去噪处理;并进行200 km/h动车组表面压力测试信号脉动压力的提取,建立了脉动压力功率谱模型.研究结果表明:该方法能有效提取出列车表面脉动压力,测点处脉动压力的幅值在20 Pa范围内时刻波动,频率主要集中在0~200 Hz;建立的脉动压力功率谱模型为列车减振和降噪提供理论指导.      

Feature Extraction of Bearing Vibration Signals Using Second Generation Wavelet and Spline-Based Local Mean Decomposition

In order to extract the fault feature frequency of weak bearing signals,we put forward a local mean decomposition(LMD)method combining with the second generation wavelet transform.After performing the second generation wavelet denoising,the spline-based LMD is used to decompose the high-frequency detail signals of the second generation wavelet signals into a number of production functions(PFs).Power spectrum analysis is applied to the PFs to detect bearing fault information and identify the fault patterns.Application in inner and outer race fault diagnosis of rolling bearing shows that the method can extract the vibration features of rolling bearing fault.This method is suitable for extracting the fault characteristics of the weak fault signals in strong noise.     

A HOS-based blind signal extraction method for chaotic MIMO systems

A novel method to extract multiple input and multiple output （MIMO） chaotic signals was proposed using the blind neural algorithm after transmitting in nonideal channel. The MIMO scheme with different chaotic signal generators was presented. In order to separate the chaotic source signals only by using the sensor signals at receivers, a blind neural extraction algorithm based on higher-order statistic （HOS） technique was used to recover the primary chaotic signals. Simulation results show that the proposed approach has good performance in separating the primary chaotic signals even under nonideal channel.     

复杂传递路径下轴承滚动体故障特征提取

为消除复杂传递路径对轴承滚动体振动信号的影响并提高故障特征提取的能力，研究了基于变分模态分解(VMD)、优化最大相关峭度解卷积(MCKD)和1.5维谱的轴承滚动体故障特征提取问题；分析了轴承滚动体原始振动信号特点、早期故障信号的特性以及复杂传递路径对振动信号的影响，运用VMD将原始振动信号分解为一系列本征模态函数(IMFs),提出了转频分量剔除方法，通过峭度准则优选2个峭度较大的IMFs分量进行重构；基于网格搜索法研究了MCKD算法参数优化方法，用以增强重构信号的周期性故障特征，消除复杂传递路径对轴承滚动体故障信号的影响；利用1.5维谱分析重构信号，建立了复杂传递路径下轴承滚动体故障特征提取新方法，实现了轴承滚动体故障的准确诊断；为了证明方法的有效性，选取美国凯斯西储大学轴承SKF6205基座滚动体数据进行试验验证与分析。试验结果表明：网格搜索法获得了MCKD算法的最优滤波长度与冲击周期参数(365、85),优化MCKD算法增强了重构信号的故障特征，减少了无关频率分量，明显降低了其他成分的干扰；提出的故障特征提取方法在0、735和1 470 W负载条件下均提取到了轴承滚动体的故障特征频...     

基于最大信噪比算法在缸盖振动信号中的分离研究

对柴油机缸盖表面振动信号进行分析处理,可以判定内部零部件的状态以及柴油机的工作状态,但实际上由于各路信号时域和频域有混叠,很难准确分离．针对现有内燃机监测诊断信号分析方法的不足,研究了基于最大信噪比的盲源分离算法．该算法将求优过程转化为广义特征值问题求解,整个过程无须迭代．通过对仿真振动信号的分离表明其有效性,从试验对象某四缸柴油机上拾取实测缸盖振动信号处理,结果显示了该算法在柴油机故障监测诊断上有良好的应用前景．     

基于SVD的柴油机振动信号盲分离预处理

在利用独立分量分析对缸盖振动信号进行盲分离时,原始采样信号不满足独立分量分析的基本假设,而且缸盖振动信号信噪比较低,影响分离的速度和精度.采用小波阈值降噪的方法进行了降噪处理,提高了信噪比.采用奇异值分解的方法估计了缸盖振动的独立源数,并对缸盖振动信号进行了白化处理,为独立分量分析的应用建立了基础.     

A Statistical Parameter Analysis and SVM Based Fault Diagnosis Strategy for Dynamically Tuned Gyroscopes

Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector machine (SVM) classification model was proposed for dynamically tuned gyroscopes (DTG). The SPA, a kind of time domain analysis approach, was introduced to compute a set of statistical parameters of vibration signal as the state features of DTG, with which the SVM model, a novel learning machine based on statistical learning theory (SLT), was applied and constructed to train and identify the working state of DTG. The experimental results verify that the proposed diagnostic strategy can simply and effectively extract the state features of DTG, and it outperforms the radial-basis function (RBF) neural network based diagnostic method and can more reliably and accurately diagnose the working state of DTG.     

典型时频分析技术在故障诊断中的应用

针对旋转机械发生故障时振动信号的不平稳性,利用典型时频分析技术对故障特征进行提取分析。该方法利用信号构造矩阵,再根据典型时频分析技术的变换特性,均值化时频分布参数,然后重构信号以实现降噪。仿真和试验结果表明,该方法能有效地提取故障特征信息,在机械故障诊断领域有广泛应用前景。     

基于本征模函数的高速磁浮线路不平顺检测

为了评估磁浮线路的不平顺程度,对实测加速度信号进行分析,发现导向系统纵向振动加速度信号对磁浮线路不平顺长波最敏感.通过对导向系统纵向振动加速度信号进行Hilbert-Huang变换,提取对应长波频率的本征模函数,求得频率族的包络瞬时幅值和瞬时频率,提取了不平顺长波.用该方法分析磁浮列车以430 km/h运行时的实测数据,得到的磁浮线路长波不平顺信号与采用大地测量法测得的结果一致.     

基于原子分解的辐射源信号二次特征提取

提出了基于原子分解的辐射源信号二次特征提取方法.在过完备多尺度Chirplet原子库基础上,首先用匹配追踪(MP)方法进行信号时频原子分解,并通过改进的量子遗传算法(IQGA)降低MP搜索过程的时间复杂性,得到表示雷达辐射源信号特征信息的最佳Chirplet原子.在此基础上,降低特征参数的维度,提取最具分类意义的原子特征向量.对5种典型雷达辐射源信号的特征提取实验表明,提取的原子特征类内聚集性强、类间分离度大,证实了本文方法的可行性和有效性.     

基于压缩感知理论的时变信号压缩技术

机械设备在线状态监测与故障诊断系统中,为实现采集数据的远程传输和实时处理,需对数据进行压缩和滤波处理.通过分析机械振动信号时变性的特点,以压缩感知理论为基础,构造了满足受限等距性质(restricted isometric property, RIP)的稀疏变换矩阵和压缩感知矩阵;提出了基于压缩感知的时变信号压缩算法,并利用Lasso算法对压缩信号进行稀疏重构,恢复原始信号.采用不同类型的时变仿真信号和实测信号进行实验,对比了提出算法与现有算法的压缩与去噪效果.实验结果表明,新算法有更好的压缩去噪效果,当压缩比为40%时,能量保持率达到了95%以上,能满足工程实际需求.      

大跨径桥梁自振特性试验的环境随机激振法

采用快速可靠的环境随机振动激振法,基于结构止各测点的脉动时域应信号,应用ＩＴＤ时域模态参数识别理论,分析大路径桥梁实桥的结构固有模态并列举了工程应用实例。     

Identification of grinding wheel wear signature by a wavelet packet decomposition method

Grinding is known as the most complicated material removal process and the method for monitoring the grinding wheel wear has its own characteristics comparing with the approaches for detecting the wear on regular cutting tools. Research efforts were made to develop the wheel wear monitoring system due to its significance in grinding process. This paper presents a novel method for identification of grinding wheel wear signature by combination of wavelet packet decomposition (WPD) based energies. The distinctive feature of the method is that it takes advantage of the combinational information of the decomposed frequency components based on the WPD so the extracted features can be customized according to the specific monitored object to get better diagnosis effects. Experiments are researched on monitoring of grinding wheel wear states under different machining conditions. The results show that the energy ratio extracted from the measured vibration signals is consistent with the grinding wheel wear condition evaluated by experiment and the further extracted feature ratio can be used in prediction of wheel wear condition.     

车辆传动系统变参小波流形融合故障诊断方法

应用流形学习方法非线性融合信号在不同小波参数下中央尺度对应的小波包络,研究了强背景噪声下车辆传动系统振动信号故障瞬态脉冲包络的有效提取问题,并与传统信号时频分解方法进行了对比研究;采用不同小波参数对振动信号进行连续小波变换,提取了每组参数下中央尺度上的小波包络;采用基尼指数选择若干包含故障瞬态脉冲信息的小波包络,构造了高维小波包络矩阵;采用局部切空间排列算法对高维小波包络进行流形融合,获得了反映故障瞬态脉冲包络本质结构的小波包络流形;为了验证所提方法的有效性和优越性,采用不同方法对轨道车辆轮对轴承和汽车变速齿轮箱的故障振动信号进行了对比分析。研究结果表明:在分析轴承外圈故障信号时,所提方法基尼指数比传统信号时频分解方法提高27.32%以上;在分析齿轮磨损故障信号时,所提方法基尼指数比传统信号时频分解方法提高26.74%以上。可见,所提方法通过综合具有不同形态的变参小波包络,可以在无需优化小波参数情况下,对车辆传动系统中的不同关键部件故障振动信号具有较好的自适应性,提取的故障脉冲包络中的带内噪声少,故障脉冲特性明显,容易识别其频谱中的故障特征频率,是检测车辆传动系统故障的一种有效方法。      

Radar Emitter Signal Recognition Using Wavelet Packet Transform and Support Vector Machines

This paper presents a novel method for radar emitter signal recognition. First, wavelet packet transform （WPT） is introduced to extract features from radar emitter signals. Then, rough set theory is used to select the optimal feature subset with good discriminability from original feature set, and support vector machines （SVMs） are employed to design classifiers. A large number of experimental results show that the proposed method achieves very high recognition rates for 9 radar emitter signals in a wide range of signal-to-noise rates, and proves a feasible and valid method.     

The Modified Multiple Model Switching Tuning Control Application in AVC

In the complex mechanical vibration environment, the dominant frequency of the system varies remarkably and swiftly under various running conditions, which also characterizes uncertainty and time-variation. It is very impending and important to suppress or isolate the detrimental vibrations related to the above memtoned system with active vibration control (AVC) technology. This paper presented the improved linear quadratic gaussian (LQG) control scheme with a specified filter to realize broadband disturbance/noise attenuation and assure intensive suppression of vibration at the key vibration frequency, then applies and modifies the multiple model switching tuning (MMST) control method by disturbance observation to track the variation of dominant vibration component timely. The effectiveness and superiority of the presented control method were demonstrated by numerical simulation and AVC experiment on a flexible cantilever beam under sweeping excitation.     

基于融合两类核Fisher鉴别函数的人脸识别方法

提出了一种基于两类核Fisher鉴别分析(KFDA)的人脸识别方法,对每2个不同人脸类别求解一个核Fisher鉴别函数,其优点是能针对特定的2个人脸图像类别,抽取区分该2类人脸的最佳鉴别特征,克服了多类KFDA和2类KFDA相比是次优的问题.为解决KFDA计算量大的问题,将MSE推广为基于核的MSE(KMSE),用其得到核Fisher鉴别函数,减少了训练和识别的计算时间.在识别阶段应用了两种融合方法融合各个基于KMSE的核Fisher鉴别函数.     

最小均方盲反卷积法在机械设备振动分析中的应用

将多通道盲反卷积应用于机械设备振动信号的分析,分析了机械振动信号的多通道盲反卷积模型,采用最小均方盲反卷积算法从测量信号中分离振源信号,并进行实验论证.实验内容包括:提取滚动轴承故障源信号,斜齿轮故障源信号以及柴油机活塞-缸套撞击信号.