基于局域波和混沌的转子系统早期碰摩故障诊断

为实现低信噪比下的微弱信号检测,提出一种基于局域波和混沌的微弱信号检测方法.将微弱的故障信号分解为有限的并且具有不同基本模式的分量,每个分量为单一成分信号,实现了信噪分离.将局域波分量输入所设计的混沌振子,混沌振子系统行为由混沌状态变为大周期运动状态,表明检测信号中含有特征成分,实现了利用混沌振子对低信噪比微弱信号的检测识别.对转子系统早期碰摩故障信号检测结果说明了该方法的有效性.     

基于多Agent的实时数据库排错研究

针对容错技术对错误处理的不彻底现状,提出从混沌信息流中自动识别并排错的新思路,彻底根除错误,完成故障的“预防“工作.排错与容错前提下的排障有着根本区别,排障是从叶子出发局部而滞后处理,排错新思路是从信号的根本特征出发,分析错误信息与有效信息之间的关系,对错误进行定义,最后提出排错的解决方案,分两步将信息流中的错误分离出来.具体实现结合多Agent技术和桶技术自适应地产生常态特征标准,用常态特征标准将混沌信息一分为二,划分出非常态信息;再进行逻辑诊断,将非常态信息又一分为二,确诊错误.研究从根本上排除与消除混沌信息流中的错误,将大规模计算转化为小规模可行的计算问题.     

基于数据采样的混沌容错同步控制方法

针对含有故障的混沌系统,利用非线性系统Euler近似离散化方法,将采样控制系统转换为离散时间系统,并针对控制器容易出现的一类故障建立数学模型,设计故障观测器,进而给出了容错同步控制器的设计方法以及误差系统渐近稳定的充分条件,有效地实现两个混沌系统的同步.所设计的控制器增益可以通过Matlab提供的线性矩阵不等式(LMI)工具箱来求解.数值仿真实现了两个蔡氏电路的同步,并通过与文献算法的对比,验证了所设计采样同步控制器的有效性和容错能力.     

采用小波变换的光伏串联电弧故障检测

建立光伏系统电弧故障实验平台,利用光伏模拟器仿真不同天气环境下的光伏阵列,对光伏系统中串联电弧故障信号进行检测和分析.采用小波变换的方法对串联电弧故障信号进行特征频带提取,并利用移动时间窗方法统计信号在小波分解后的高频系数的能量值,用其表征电弧故障信号的杂乱度和混沌度.研究结果表明:该检测方法为快速准确地诊断串联电弧故障提供有效判据.     

Nonlinear fault diagnosis method based on kernel principal component analysis

To ensure the system run under working order, detection and diagnosis of faults play an important role in industrial process. This paper proposed a nonlinear fault diagnosis method based on kernel principal component analysis (KPCA). In proposed method, using essential information of nonlinear system extracted by KPCA, we constructed KPCA model of nonlinear system under normal working condition. Then new data were projected onto the KPCA model. When new data are incompatible with the KPCA model,it can be concluded that the nonlinear system is out of normal working condition. Proposed method was applied to fault diagnosis on rolling bearings. Simulation results show proposed method provides an effective method for fault detection and diagnosis of nonlinear system.     

基于改进DFA和LDA的永磁同步电机机械故障检测

为提高故障检测的精度，研究了变转速工况下永磁同步电机的机械故障检测方法.首先，分析了电机轴承、转子偏心及其复合故障的振动特性；其次，采用Vold-Kalman算法对故障特征分量进行跟踪提取，并通过信号重构消除转速变化对故障特征分量的影响；提出一种基于改进去趋势波动分析和线性判别式分析的机械故障检测方法，实现对重构信号的故障特征提取和故障检测；最后，对所提出故障检测方法的有效性进行实验验证.实验结果表明文中所提出方法的故障检测精度为88%.     

混沌同步系统降噪方法及其在滚动轴承故障诊断中的应用

提出了一种基于混沌同步系统的降噪方法,将其应用于滚动轴承振动信号的前期处理,并结合功率谱密度进行故障诊断.分析Chua电路混沌同步系统的降噪机理,讨论系统处于不同运行状态时,输入信号对相空间运行轨迹的影响;搭建混沌同步系统降噪模型,分析其检测特性,并与其他方法进行比较;将滚动轴承不同损伤模式下测得的振动信号输入该模型,比较振动信号和同步误差信号的时域波形和功率谱密度,并通过峰值查找,匹配对应的故障特征频率.新方法利用了混沌系统的噪声免疫性和可同步性,避免了现有方法参数设定复杂和运行状态判定困难的问题.结果表明该方法可有效提升被测信号的信噪比,能与传统方法结合形成新的故障诊断方法.      

阱宽非对称性诱导随机共振的轴承故障诊断方法

针对随机共振能够俘获噪声能量增强与提取机械微弱故障特征的优点,基于两态信噪比理论研究了阱宽非对称性诱导下的随机共振现象,理论结果表明阱宽诱导下的非对称随机共振比对称随机共振具有更高的输出信噪比,意味着适当的非对称性能够改善随机共振的增强性能。因此,提出了阱宽非对称性诱导随机共振的轴承故障诊断方法,利用量子遗传算法以信噪比为目标函数优化阱宽非对称性,以获取阱宽非对称性与微弱故障特征之间的最佳匹配。仿真和轴承实验结果表明,提出的方法能够有效地实现轴承的故障诊断,而且其性能优于集成经验模式分解。     

Fault Identification of Rotor Machine Based on Optimized Method

The size and complexity of modern equipment require more advanced fault diagnosis techniques Different from signal analysis based methods, a dynamic model based diagnosis technique can further diagnose the location and severity of the fault, and detect multiple faults at one time. A model based fault diagnosis method was developed to identify typical faults of rotating machinery. This method can identify mass unbalances, crack locations and sizes, and oil film parameters in rotating machinery by optimization methods and dynamics simulation technique. Numerical and experimental results demonstrate that the method is useful for detecting faults of rotating systems.     

基于ARMAX模型的挖掘机液压系统故障检测

根据挖掘机的可靠性水平和智能化程度,提出了一种基于ARMAX(Auto-Regressive Moving Average Exogenous)模型的挖掘机液压系统故障检测方法.使用系统正常状态下的信号样本建立系统的ARMAX辨识模型,通过休哈特控制图分析ARMAX辨识模型的输出残差,以获取故障检测的阈值;然后,将系统故障状态下的信号样本代入ARMAX辨识模型,当模型残差超过阈值,即能判定系统发生故障.实验分析表明,基于ARMAX模型的故障检测方法够有效地检测挖掘机液压系统产生的故障.图3,参10.     

基于龙格库塔算法和可编程门阵列技术的混沌系统实现

提出了使用硬件描述语言(HDL)在现场可编程逻辑门阵列器件(FPGA)上实现二阶龙格库塔法产生混沌信号的一种新方法.首先,根据二阶龙格库塔算法分解求解连续混沌系统,得到一个迭代求解过程;其次,使用HDL描述状态机实现该迭代过程,输出数字混沌序列;最后,将数字混沌序列输出至高速数模转换器(DAC),可观察到模拟混沌信号.给出了网格状多卷波混沌系统和经典Lorenz系统上的具体实现步骤和相应结果.结果表明,此方法具有一定的普适性,可用于其它混沌系统的混沌信号产生,且消耗FPGA资源不多,具有很强实用性.     

Ant colony optimization approach for test scheduling of system on chip

It is necessary to perform the test of system on chip, the test scheduling determines the test start and finishing time of every core in the system on chip such that the overall test time is minimized. A new test scheduling approach based on chaotic ant colony algorithm is presented in this paper. The optimization model of test scheduling was studied, the model uses the information such as the scale of test sets of both cores and user defined logic. An approach based on chaotic ant colony algorithm was proposed to solve the optimization model of test scheduling. The test of signal integrity faults such as crosstalk were also investigated when performing the test scheduling. Experimental results on many circuits show that the proposed approach can be used to solve test scheduling problems.     

一种提高诊断信息质量的方法

针对工程实际中噪声干扰、不同源信号之间的混叠及信号的信噪比低，造成信号分析和特征提取难的问题，研究了采用连续小波变换（CWT）和独立分量分析（ICA）的方法对滚动轴承的声音信号进行了消噪和分离，从而提高了诊断信号的信噪比，保证了故障的确诊。通过仿真实验和实例分析，验证了该方法的有效性。     

一种GAF-CNN行星齿轮箱故障诊断方法

为将深度学习识别2D图像的优势应用于行星齿轮箱故障诊断,提出一种格拉姆角场-卷积神经网络行星齿轮箱故障诊断模型.利用格拉姆角场中的格拉姆角差场和格拉姆角和场两种方法,将行星齿轮箱振动信号转化为2D图像,提取图像特征并输入优化后的卷积神经网络模型,最终获得理想的识别精度.通过研究网络参数、不同网络层对故障诊断模型的影响,构建模型的最优组合.试验和对比结果表明,格拉姆角差场卷积神经网络比格拉姆角和场卷积神经网络具有更高的识别精度,在行星齿轮箱故障诊断方面的效果优于其他智能算法.      

基于FastICA的高压直流输电线路故障测距方法

提出了采用快速独立分量分析(FastICA)的高压直流输电线路(HVDC)单端行波故障测距方法。利用FastICA算法对故障后的直流输电线路电流信号进行处理,分离出电流行波特征信号,检测初始行波波头与第二个行波波头到达测量点的时间,并判断这两个波头的极性关系,实现了故障测距。应用Matlab软件,建立了HVDC系统模型,对多种故障类型进行了故障测距仿真。结果表明,该方法可准确区分中点内外故障,正确有效。     

Diagnosis of multiple faults using a double parallel two-hidden-layer extreme learning machine

Multiple faults are easily confused with single faults.In order to identify multiple faults more accurately,a highly efficient learning method is proposed based on a double parallel two-hidden-layer extreme learning machine,called DPTELM.The DPT-ELM method is a variant of an extreme learning machine(ELM).There are some issues with ELM.First,achieving a high accuracy requires too many hidden nodes;second,the direct connection between the input layer and the output layer is ignored.Accordingly,to deal with the above-mentioned problems,DPT-ELM extends the single-hidden-layer ELM to a two-hidden-layer ELM,which can achieve a desired performance with fewer hidden nodes.In addition,a direct connection is built between the input layer and the output layer.Since the input layer weights and the thresholds of the two hidden layers are determined randomly,this simplifies the improved model and shortens the calculation time.Additionally,to improve the signal to noise ratio(SNR),an adaptive waveform decomposition(AWD)algorithm is used to denoise the vibration signal.Then,the denoised signal is used to extract the eigenvalues by the time-domain and frequency-domain methods.Finally,the eigenvalues are input to the DPT-ELM classifier.In this paper,two groups of rolling bearing data at different speeds,which were collected from a real experimental platform,are used to test the method.Each set of data includes three single fault states,two complex fault states and a healthy state.The experimental results demonstrate that the DPT-ELM method achieves fast learning speed and a high accuracy.Moreover,based on 10-fold cross-validation,it proves to be an effective method to improve the accuracy with fewer hidden nodes.     

Soft Fault Diagnosis for Analog Circuits Based on Slope Fault Feature and BP Neural Networks

Fault diagnosis is very important for development and maintenance of safe and reliable electronic circuits and systems. This paper describes an approach of soft fault diagnosis for analog circuits based on slope fault feature and back propagation neural networks (BPNN). The reported approach uses the voltage relation function between two nodes as fault features; and for linear analog circuits, the voltage relation function is a linear function, thus the slope is invariant as fault feature. Therefore, a unified fault feature for both hard fault (open or short fault) and soft fault (parametric fault) is extracted. Unlike other NN-based diagnosis methods which utilize node voltages or frequency response as fault features, the reported BPNN is trained by the extracted feature vectors, the slope features are calculated by just simulating once for each component, and the trained BPNN can achieve all the soft faults diagnosis of the component. Experiments show that our approach is promising.     

基于LMD和AR模型的转子系统故障诊断方法

提出了基于局部均值分解(Local mean decomposition,简称LMD)和AR模型相结合的转子系统故障诊断方法.该方法先用LMD方法将转子振动信号分解成若干个瞬时频率具有物理意义的PF(Product function,简称PF)分量之和,然后对每一个PF分量建立AR模型,提取模型参数和残差方差作为故障特征向量,并以此作为神经网络分类器的输入来识别转子的工作状态和故障类型.与EMD方法的对比研究表明,这两种方法均能有效地应用于转子系统的故障诊断.但LMD方法信号分解后数据残差比EMD方法的小.     

基于CMLE的改进型IR-UWB同步算法

提出了一种基于CMLE的改进型IR-UWB同步算法,包含3项优化方案:帧级噪声抑制、噪声模板互相关估计以及滑动相关搜索.其中,帧级噪声抑制、噪声模板互相关估计通过加强噪声抑制能力从而改善算法在低信噪比情况下的参数估计精度;滑动相关搜索进一步优化了高信噪比情况下参数估计的均方误差性能.数学分析及仿真实验的结果表明每项优化方案均在不同程度上实现了预期的性能优化.     

An approach to 3D NURBS modeling of complex fault network considering its historic tectonics

Fault disposal is a research area that presents difficulties in 3D geological modeling and visualization. In this paper, we propose an integrated approach to reconstructing a complex fault network (CFN). Based on the non-uniform rational B-spline (NURBS) techniques, fault surface was constructed, reflecting the regulation of its spatial tendency, and correlative surfaces were enclosed to form a fault body model. Based on these models and considering their historic tectonics, a method was put forward to settle the 3D modeling problem when the intersection of two faults in CFN induced the change of their relative positions. First, according to the relationships of intersection obtained from geological interpretation, we introduced the topological sort to determine the order of fault body construction and rebuilt fault bodies in terms of the order; then, with the disposal method of two intersectant faults in 3D modeling and applying the Boolean operation, we investigated the characteristic of faults at the intersectant part. An example of its application in hydropower engineering project was proposed. Its results show that this modeling approach can increase the computing efficiency while less computer memory is required, and it can also factually and objectively reproduce the CFN in the engineering region, which establishes a theoretical basis for 3D modeling and analysis of complex engineering geology.