旋转设备数据不平衡问题的数据生成方法

在旋转设备运行状态监测及故障识别时，采集的样本多为无故障样本，而故障样本较少，这种数据分布的不平衡会严重影响分类器识别的准确性。针对此问题，提出了一种少数样本数据生成方法，即基于傅里叶变换与皮尔逊系数优化的生成对抗神经网络（Fourier-Pearson generative adversarial networks，简称FP-GAN）模型。通过对故障少数样本的扩充，提高故障诊断训练和识别的准确性。首先，使用傅里叶变化得到信号频域的单边谱，使用GAN网络生成信号频域；其次，通过皮尔逊相关系数对生成的数据进行优化；最后，通过傅里叶逆变换获得更接近真实数据的生成数据。仿真和实验数据验证表明，基于FP-GAN生成的数据样本在时域特征、时域统计特征以及分类器分类结果方面都能较好地与已有实际数据融合，可以对小样本数据进行增强，能有效解决数据不平衡问题。     

基于信息融合的滚动轴承故障诊断

针对传统的单一传感器检测准确率不高,诊断系统不稳定等问题,将振动和声发射2种检测方法进行融合。首先对采集到的2种信号进行小波降噪及Hilbert解调,得到故障信号的频域包络谱,计算其频段能量值并组成特征向量;然后利用BP神经网络建立多传感器的信息融合系统,选取合适的样本输入网络进行训练,直至达到所要求的误差范围;最后实现对样本轴承的故障诊断,达到了相对较高的诊断正确率。     

滚动轴承故障程度诊断的HMM方法研究

为提高滚动轴承故障诊断率,提出基于时频域指标的HMM轴承故障程度诊断方法。利用轴承故障模拟试验台,采集不同剥落程度滚动体的振动信号,分别提取均方值、有效值、方差、修正样本方差、标准差、频域中心及带宽共7个时域和频域指标作为特征向量训练HMM,得到基于HMM的诊断分类器。利用该分类器对330组待检滚动轴承振动信号进行分析,诊断正确率达90%以上,说明该方法能有效提取故障特征。     

基于LMD能量熵和支持向量机的齿轮箱故障诊断

针对小样本情况下齿轮箱复合多种故障特征难以提取和分类的问题,提出了基于局部均值分解（LMD）能量熵和支持向量机（SVM）的故障诊断方法。首先利用LMD方法对采集的齿轮箱振动信号进行分解,得到有限个PF分量;然后根据不同故障下齿轮箱振动信号在频域区间内分布不均的特性,分析出PF分量能量在不同频域范围离散情况,即求出LMD能量熵;最后利用SVM多故障分类器对提取出的特征展开训练和测试,进行齿轮箱故障分类。实验结果显示,即使在小样本情况下,且同时存在非单一、多种齿轮箱故障时,基于LMD能量熵和SVM方法也可以对齿轮箱故障进行特征提取和精准分类,实现齿轮箱故障诊断。     

基于堆叠稀疏去噪自动编码网络与多隐层反向传播神经网络的铣刀磨损预测模型

刀具磨损状态是机械加工过程中需考虑的重要因素之一.针对铣刀磨损的在线预测问题,建立了一种基于深度学习的铣刀磨损预测模型.首先,将采集到的铣刀切削时的振动信号进行小波去噪后,利用快速傅里叶变换和小波包分解等技术提取时域、频域及时频域上的特征参数,并根据相关性分析从中筛选出合格的特征参数合并为特征向量,以此作为堆叠稀疏去噪自动编码网络(SSDAE)的含噪样本.其次,利用特征后处理的方式对已经筛选出的特征参数进行单调不递减及平滑处理,并将其作为SSDAE的无噪样本来训练该网络.然后,将经过SSDAE降维后的特征向量作为多隐层反向传播神经网络(BPNN)的输入,以这些特征对应的实际铣刀的磨损量作为标签对该网络进行拟合训练.最后,对训练好的模型进行实验验证,通过测试数据集和人为加入噪声的测试数据集的对比,结果显示所提模型不仅具有较高的预测精度,还具有较高的鲁棒性.     

基于DBN模型的航空液压管路故障诊断方法

为了解决航空发动机液压管路系统中管路故障诊断困难的问题,提出了一种基于深度置信网络(Deep Belief Networks,DBN)的航空液压管路智能故障诊断方法.首先,将采集的液压管路振动数据进行处理,提取出时频域特征参数,其次,将时频域特征参数作为输入样本,输入到深度置信网络模型中,利用深度置信网络模型进行液压管路故障的识别;最后,将本方法应用于航空液压管路模拟故障实验数据中,同时将本文方法与BPNN和SVM等方法进行对比分析,结果表明:本方法对液压管路故障的总体准确率达到99.27％,平均AUC值达到0.993 7,同时表明本文建立的分类模型不仅能够实现航空液压管路状态的准确分类,而且对于管路单一故障和多故障并发情况也能精准识别.     

基于SURF特征的汽车轮毂型号识别

提出了一种基于SURF特征与神经网络相结合的方法,实现了对多种型号轮毂的识别.首先,将所有采集的样本图像分成训练集、验证集和测试集;然后,将所有样本图像缩放至合理大小并进行转灰操作;提取所有样本图像的SURF特征并导入到搭建好的神经网络中进行训练、验证和测试,通过神经网络强大的学习能力,从对大量特征数据的学习过程中获得一个最佳的识别模型.该方法可以以最少的样本图像获得一个能准确识别轮毂型号的识别模型.另外,该方法鲁棒性好、抗干扰能力强,能满足自动化生产线实时性的要求.     

基于MGAN和CNN的齿轮故障智能诊断方法

针对实际工程中采集的齿轮故障信号样本不足,以及在噪声干扰的情况下采用常见深度学习网络进行模式识别出现的训练不足、故障识别率低和生成对抗网络易发生模式坍塌等问题,提出了一种基于MGAN(Mixture generative adversarial nets)和CNN(Convolutional neural networks)的齿轮故障智能诊断方法。将真实齿轮信号经形态学滤波处理后通过时频变换技术转变为时频信号,利用MGAN网络合成新样本来获得一个平衡数据集,克服了样本不足的问题;同时,分析了MGAN网络主要参数对合成样本质量的影响规律;采用平衡数据集训练CNN进行故障诊断,有效提高了故障诊断率。通过对比试验和试验台试验,验证了该方法在样本不足条件下对故障进行准确识别以及克服神经网络模式坍塌方面的有效性及优势,为实现齿轮箱典型故障提取及智能识别提供了新的研究思路。     

PCA-SAE的齿轮箱故障诊断方法研究

针对因长时间的信号采集使得振动信号面临数据量大的问题。传统的信号分析方法,已无法解决大数据情况下故障的特征提取与分类,同时采集到的数据样本具有多维度多样本的情况,导致训练网络时在前期导入数据阶段耗费大量时间与硬件的内存,并且会导致网络训练中产生过拟合现象,影响分类准确率。针对以上问题本文提出基于主成分分析与堆叠自动编码机相结合的齿轮故障诊断研究,以实现对齿轮振动信号快速准确的特征提取与分类。首先对原始信号进行主成分析,得到各主成分贡献率,其次,选取主成分贡献率高的前几列作为深度学习网络输入样本。最后深度学习网络即堆叠自动编码机网络对训练数据集进行学习提取数据中的特征并应用测试数据集部分进行分类并计算分类的准确率。最终,实验中将所提深度学习方法与传统的特征提取方法和分类方法进行比较最终识别精度进行比较。实验结果表明本文所提方法最终可以达到98.6%的准确率,实现端到端的故障诊断方法,可以很好的应用于故障诊断领域。     

基于优化CNN与信息融合的地铁牵引电机轴承故障诊断

针对在单一传感器下轴承故障识别率低的问题,提出一种基于优化CNN与信息融合的地铁牵引电机轴承故障智能检测方法。首先,选取NU216轴承为研究对象,预制故障缺陷;然后,采用正交试验法设计试验方案,采集NU216轴承的振动信号和声发射信号;其次,将原始数据通过连续小波变换,分别提取轴承的振动和声发射信号的时频域特征,并将2类单通道数据进行融合,得到双通道融合数据集;最后,将得到的3类数据集分别划分为训练集和测试集,输入优化后的卷积神经网络模型进行训练、测试。试验结果表明,基于振动信号的故障诊断准确率为95.76%,基于声发射信号的故障诊断准确率为92.33%,基于融合信号的故障诊断准确率为98.59%。     

Minimal Realization of Linear Graph Models for Multi-physics Systems

An engineering system may consist of several different types of components,belonging to such physical"domains"as mechanical,electrical,fluid,and thermal.It is t...     

INTEGRAL EQUATION METHOD＇S APPLICATION IN HOLE-EDGE STRESS OF COMPOSITE MATERIAL PLATE WITH DIFFERENT SHAPED HOLES

The strength of composite plate with different hole-shapes is always one of the most important but complicated issues in the application of the composite material. The holes will lead to mutations and discontinuity to the structure. So the hole-edge stress concentration is always a serious phenomenon. And the phenomenon makes the structure strength decrease very quickly to form dangerous weak points. Most partial damage begins from these weak points. According to the complex variable functions theory, the accurate boundary condition of composite plate with different hole-shapes is founded by conformal mapping method to settle the boundary condition problem of complex hole-shapes. Composite plate with commonly hole-shapes in engineering is studied by several complex variable stress fimction. The boundary integral equations are founded based on exact boundary conditions. Then the exact hole-edge stress analytic solution of composite plate with rectangle holes and wing manholes is resolved. Both of offset axis loadings and its influences on the stress concentration coefficient of the hole-edge are discussed. And comparisons of different loads along various offset axis on the hole-edge stress distribution of orthotropic plate with rectangle hole or wing manhole are made. It can be concluded that hole-edge with continuous variable curvatures might help to decrease the stress concentration coefficient; and smaller angle of outer load and fiber can decrease the stress peak value.     

Non-monotonic material contrast in scanning ion and scanning electron images

30 keV Ga⁺ focused ion beam induced secondary electron (iSE) imaging was used to determine the relative contrast between several materials. The iSE signal compared from C, Si, Al, Ti, Cr, Ni, Cu, Mo, Ag, and W metal layers does not decrease with an increase in target atomic number Z₂, and shows a non-monotonic relationship between contrast and Z₂. The non-monotonic relationship is attributed to periodic fluctuations of the stopping power and sputter yield inherent to the ion–solid interactions. In addition, material contrast from electron-induced secondary electron (eSE) and backscattered electron (BSE) images using scanning electron microscopy (SEM) also shows non-monotonic contrast as a function of Z₂, following the periodic behavior of the stopping power for electron–solid interactions. A comparison of the iSE and eSE results shows similar relative contrast between the metal layers, and not complementary contrast as conventionally understood. These similarities in the contrast behavior can be attributed to similarities in the periodic and non-monotonic function defined by incident particle–solid interaction theory.     

Application of Feature Extraction through Convolution Neural Networks and SVM Classifier for Robust Grading of Apples

This paper proposes a novel grading method of apples,in an automated grading device that uses convolutional neural networks to extract the size,color,texture,an...     

分布动态载荷识别的抗噪处理

针对正交多项式频域法在用多种响应对矩形薄板进行载荷识别中抗噪性较差的问题,综合运用平均法、矩阵预处理和奇异值截断法等方法对之进行改善,并引入空间映射的思想,将该方法的应用范围拓展为复杂的模型.利用仿真算例,证实了该方法具有较好的抗噪性.     

基于MELTAC-N平台的核电厂安全级DCS环网研究与测试

针对工程实践中环网通讯相关问题的处理缺乏理论基础及国产化安全级DCS平台的开发缺乏成熟经验借鉴问题,对基于MELTAC-N平台核电厂安全级DCS环网的软硬件实现进行了研究。提出了安全级DCS环网双环网冗余设计、光切换开关设计等硬件设计方法,以及以RPR协议为基础,采用全数据收发策略的软件设计方法。在CPR1000安全级DCS平台上对安全级DCS环网的可靠性及实时性进行了评价,并进行了容错能力、响应时间及响应时间稳定性测试验证实验。结果表明,基于MELTAC-N平台安全级DCS环网软硬件设计具有较好的容错能力及响应时间稳定性。     

Short-Term Relay Quality Prediction Algorithm Based on Long and Short-Term Memory

The fraction defective of semi-finished products is predicted to optimize the process of relay production lines, by which production quality and productivity ar...     

End-to-End Multiview Gesture Recognition for Autonomous Car Parking System

The use of hand gestures can be the most intuitive human-machine interaction medium.The early approaches for hand gesture recognition used device-based methods....     

The laboratory simulation of abrasive wear

Abrasive wear has long been recognised as one of the most potentially serious tribological problems facing the operators of many types of plant and machinery; several industrial surveys have indicated that wear by abrasion can be responsible for more than 50% of unscheduled machine and plant stoppages. Locating the operating point of a tribological contact in an appropriate operational ‚map’︁ can provide a useful guide to the likely nature and origins of the surface degradation experienced in use, though care must be exercised in choosing the most suitable parameters for the axes of the plot. Laboratory testing of materials and simulations of machine contacts are carried out for a number of purposes; at one level for the very practical aims of ranking candidate materials or surface hardening treatments in order of their wear resistance, or in an attempt to predict wear lives under field conditions. More fundamentally, tests may be aimed at elucidating the essential physical mechanisms of surface damage and loss, with the longer term aim of building an analytical and predictive model of the wear process itself. In many cases, component surface damage is brought about by the ingress of hard, particulate matter into machine bearing or sealing clearances. These may be running dry although, more usually, a lubricant or service fluid is present at the interface. A number of standardised wear test geometries and procedures have been established for both two- and three-body wear situations, and these are briefly described. Although abrasive wear is often modelled as following an ‚Archard’︁ equation (i.e. a linear increase in material loss with both load and time, and an inverse dependence on specimen hardness) both industrial experience and laboratory tests of particularly lubricated contacts show that this is not always the case: increasing the hardness differential in an abrasively contaminated lubricated pair may not always reduce the rate of damage to the harder surface.     

Limits of topographic measurement by the scanning tunnelling and atomic force microscopes

Parameters describing the topographic character of a surface (height, surface wavelength, slope and curvature) can be derived from equivalent sinusoidal profiles. The response of a surface-measuring instrument may be modelled in terms of instrument parameters such as stylus radius, and scanning range and resolution. The performance of the instrument may then be mapped as a zone in amplitude-wavelength (AW) space to show the sinusoidal profiles it is capable of measuring. In a first-order analysis the STM and AFM are considered as equivalent to contact-stylus instruments with a notional stylus radius equal to the tip radius plus the gap. Comparisons between different instruments and types of instrument are readily made by mapping in AW space. The error arising from convolution of the sinusoidal profile with that of the finite tip may be quantified and plotted as contours in AW space.