水轮发电机组状态监测和故障诊断系统

将Internet网络技术和虚拟仪器技术用于水轮发电机组的状态监测和故障诊断,使水轮发电机组状态监测和故障诊断提升到远程的网络环境水平,以切实提高状态监测和故障诊断的水平和时效性.通过采用Client/Server网络体系结构,运用LabIEW、DataSocket、PXI和MXI-3等技术,实现了水轮发电机组在线状态监测和故障诊断,为水轮发电机组的状态检修提供了可靠的技术保证,具有良好的应用前景.     

基于LabVIEW的齿轮故障诊断研究

基于虚拟仪器的设备故障监测诊断技术是一门将设备监测诊断技术与虚拟仪器相结合的新技术.本文在LabVIEW开发平台上进行齿轮的故障监测诊断模块的开发,通过在被监测设备上建立状态监测点,采集设备的振动信号并获得运行状态数据,从而实现齿轮故障监测诊断的功能.     

基于故障诊断的虚拟仪器系统研究

如何及时诊断故障、预测设备运行状态的发展趋势,成为当前研究的一个热点;如何开发集成软硬件结合的系统进行实时、在线诊断,将是诊断成败的关键,虚拟仪器技术的迅速发展,将故障诊断与电子测试技术引入到更高的层次领域,这是测试技术与故障诊断技术相结合发展的重要趋势。介绍了虚拟仪器技术的特点和构成,在此基础上将现代信号处理新理论高阶累积量(HOC)引入到虚拟仪器的信号分析之中,对传动系统的故障进行了特征提取和分析,以VC++软件开发平台为基础,并结合MATLAB语言研制和开发出了机械故障诊断虚拟仪器系统,设计了各种功能模块,包括振动信号的分析、处理和系统故障诊断等。从而实现了故障诊断虚拟仪器系统应有的功能。     

数控机床加工系统远程在线监测方案设计

本文以数控机床为对象,设计了一个基于网络技术和虚拟仪器技术的数控机床加工系统远程监测系统,并将当前流行的视频监控技术应用到该在线监测系统中来,重点研究了刀具状态的监测方法。     

基于WSN和数据融合技术的电力变压器故障诊断系统的研究

探讨一种基于数据融合和可拓理论的电力变压器故障诊断方法,基于此方法,应用无线传感器网络技术和.NET开发平台构建和开发出一套电力变压器故障诊断系统。该系统将WSN技术用于电力变压器油中溶解气体的无线数据采集,实现变压器油中溶解气体的实时数据采集和传送;同时运用信息融合技术对数据进行优化处理,既可以降低网络能耗又保证了数据的准确和客观;在变压器进行故障诊断时,针对现有电力变压器故障诊断的气体比值法存在的编码规则不完整、诊断故障类型单一等缺陷,使用可拓诊断方法,定量与定性相结合,使变压器故障诊断方法更为精细化。在线故障诊断可视化系统可以实现变压器油中溶解气体参数的采集、显示、分析、存储和回放等功能,实现对变压器故障信息的实时分析与自动诊断,既解决了作业人员远程实时监测变压器运行状态问题,又大大提高了变压器故障诊断的性能,为变压器在线监测和故障诊断技术提供新的发展方向。     

一种基于神经网络在线逼近器的非线性鲁棒故障诊断方法

针对一类不确定非线性动态系统，提出了一种基于神经网络在线逼近结构的鲁棒故障 检测方法．该方法通过构造神经网络通过在线逼近结构学习非线性故障特性来监测动态系统 的反常行为,当故障发生时，在线估计器可逼近各种可能的未知故障，然后对其进行诊断和 适应．神经网络权重的在线学习律没有持续激励的要求，并采用Lyapunov稳定性理论保证了 闭环误差系统一致最终有界稳定．     

网络化水电机组振动监测和故障诊断系统

针对水电机组振动监测与故障诊断的多样性和复杂性,运用PXI总线仪器和LabVIEW可视化的虚拟仪器系统开发平台,设计并组建网络化水电机组在线振动监测和故障诊断系统.该系统实现了水电机组振动信号的数据采集、数据处理和分析,并能通过网络进行故障诊断.实际应用证明这种基于虚拟仪器技术的网络化水电机组在线振动监测和故障诊断系统具有很强的灵活性和可扩展性.     

基于虚拟仪器技术的谐波在线监测系统的设计与实现

一种基于虚拟仪器技术的谐波在线监测系统的设计方法，可以用于电力系统谐波在线远程监测。文中论述了监测系统的硬件结构、软件平台及应用软件的工作模式。通过应用虚拟仪器技术和网络技术进行谐波测量和数据传输处理，并得出实验结果，从而实现了电力系统谐波远程监测的目的。     

基于虚拟仪器机械动力参数自动测试系统的开发

本文介绍了一种基于虚拟仪器的机械动力参数的微机自动测试测试系统，并对该系统的工作原理、硬件结构及软件编程进行了阐述。该系统综合采用了虚拟仪器技术和现代测控技术来实现对机械动力参量的的在线监测和管理。实践表明该系统性能良好。     

水轮发电机组振动监测及其在远程故障诊断中的应用

本文介绍了基于虚拟仪器技术研究而用实时嵌入式操作系统实现的水轮发电机组振动的监测系统．该智能实时远程故障诊断系统具有推广应用的价值。     

Model-based real-time thermal fault diagnosis of Lithium-ion batteries

Ensuring safety and reliability is a critical objective of advanced Battery Management Systems (BMSs) for Li-ion batteries. In order to achieve this objective, advanced BMS must implement diagnostic algorithms that are capable of diagnosing several battery faults. One set of such critical faults in Li-ion batteries are thermal faults which can be potentially catastrophic. In this paper, a diagnostic algorithm is presented that diagnoses thermal faults in Lithium-ion batteries. The algorithm is based on a two-state thermal model describing the dynamics of the surface and the core temperature of a battery cell. The residual signals for fault detection are generated by nonlinear observers with measured surface temperature and a reconstructed core temperature feedback. Furthermore, an adaptive threshold generator is designed to suppress the effect of modelling uncertainties. The residuals are then compared with these adaptive thresholds to evaluate the occurrence of faults. Simulation and experimental studies are presented to illustrate the effectiveness of the proposed scheme.     

Sensor fault tolerant control of nonlinear Takagi–Sugeno systems. Application to vehicle lateral dynamics

This paper presents a new scheme for sensor fault tolerant control for nonlinear systems based on the Takagi–Sugeno modeling. First, a structured residual generator aimed at detecting and isolating sensor faults is designed. A bank of observers controlled either by only one system output or a set of outputs is then implemented, leading to a set of state estimates. The parallel distributed compensation structure is adopted to design the fault tolerant controller. The novelty in this paper is that the estimated state used in the controller is a weighted state vector obtained from all the estimated states provided by the different observers. The weighting functions depend on the residual vector signals delivered by the residual generator. They are designed to avoid crisp switches in the control law. Indeed, the interesting feature of the proposed approach is to avoid the commonly used switching strategy. For each residual component, the greater its magnitude is, the less the weight affected to the corresponding state estimate is. Consequently, the controller only uses estimations computed on the basis of healthy measurements. The closed‐loop stability is studied with the Lyapunov theory, and the obtained conditions are expressed as a set of linear matrix inequalities. The proposed residual generation and fault tolerant controller are applied to a vehicle lateral dynamics affected by sensor faults. Copyright © 2015 John Wiley & Sons, Ltd.     

远程宽带故障诊断系统的结构及技术研究

研究了基于宽带网络和虚拟现实技术的远程故障诊断系统的结构、数据管理模式以及关键技术。电厂发电机组的振动数据在现场作初步故障分析和诊断后，通过宽带Internet传输到远程诊断中心的数据库服务器中。诊断中心的状态监测工作站实时地显示和监控机组的运行信息，并利用虚拟现实技术对机组现场状态作三维动画模拟，使远程专家真实地了解电厂机组的运行状况，作出正确的故障判断。故障诊断的专家系统能自动地对机组信息进行智能化的推理和诊断，实现信息的交互传递。     

A multiple observers and dynamic weighting ensembles scheme for diagnosing new class faults in wind turbines

This paper presents an incremental way to design the decision module of a diagnostic system by resorting to dynamic weighting ensembles of classifiers. The method is applied for sensor fault detection and isolation in a doubly fed induction generator for wind turbine application. Three sets of observers are combined to generate residuals that are robust to operating point changes. These signals are progressively fed into a dynamic weighting ensembles algorithm, called Learn⁺⁺.NC, for fault classification. The algorithm incrementally learns the residuals–faults relationships and dynamically classifies the faults including multiple new classes. It resorts to a dynamically weighted consult and vote mechanism to combine the outputs of the base-classifiers.     

Vibrant fault diagnosis for hydroelectric generator units with a new combination of rough sets and support vector machine

The fault diagnosis for hydroelectric generator unit (HGU) is significant to prevent dangerous accidents from occurring and to improve economic efficiency. The faults of HGU involve overlapping fault patterns which may denote a kind of faults in the early stage or a subset of samples that caused by multi-fault. But until now it has not been considered in the traditional classifier of fault diagnosis for HGU. In this paper, a novel classifier combined rough sets and support vector machine is proposed and applied in the fault diagnosis for HGU. Instead of classifying the patterns directly, the fault patterns lying in the overlapped region are extracted firstly. Then, upper and lower approximations of each class are defined on the basis of rough set technique. Next, for the fault patterns lying in the overlapped region, the reliability they belong to a certain class is calculated. At last, the proposed method is successfully applied in analyzing an international standard data set, as well as diagnosing the vibrant faults of a HGU. The results show that the proposed classifier can more properly describe the complex map between the faults and their symptoms, and is suitable to fault diagnosis for HGU.     

基于RBF神经网络和小波包的电动机故障诊断研究

针对传统的电动机故障诊断存在很难准确提取故障时的特征信号及对故障作出准确预测的问题,提出了一种基于RBF神经网络和小波包的电动机故障诊断的方法。该方法采用小波包分析技术提取电动机典型轴承故障、转子故障和绝缘故障振动信号的特征频段能量并组成向量作为RBF神经网络的输入,用于诊断电动机的故障。实验和仿真结果表明,使用RBF神经网络对电动机故障诊断是非常有效的,对电动机早期故障的发现及维修有积极意义。     

基于非线性输出频率响应函数的液压管路故障诊断研究

航空液压管路系统受到多源激励的影响,在工程应用中经常发生管路系统振动故障。针对长跨距管路系统振动故障定位困难的问题,采用光纤布拉格光栅（Fiber Bragg Grating,FBG）测试技术开展了长跨距管路系统振动信号分布式测量,并提出结合非线性输出频率响应函数（Nonlinear Output Frequency Response Function,NOFRF）分析方法实现液压管路系统卡箍松动和管体碰撞故障的准确定位。采用FBG传感器对长跨距管路系统振动信号进行分布式测量,克服了传统加速度测试过程中的电磁干扰和复杂布线问题;采用NOFRF指标提取卡箍松动和管体碰撞的故障特征,实现了对微弱故障的早期监测和准确定位。试验结果表明,所提方法可有效定位卡箍松动和管体碰撞故障。     

Elegant antidisturbance fault‐tolerant control for stochastic systems with multiple heterogeneous disturbances

In this article, the elegant antidisturbance fault‐tolerant control (EADFTC) problem is studied for a class of stochastic systems in the simultaneous presence of multiple heterogeneous disturbances and time‐varying faults. The multiple heterogeneous disturbances include white noise, norm bounded uncertain disturbances and uncertain modeled disturbances with multiple nonlinearities and unknown amplitudes, frequencies, and phases. The time‐varying fault signals are caused by lose efficacy of actuator. To online estimate uncertain modeled disturbances and time‐varying faults, a novel composite observer structure consisting of the adaptive nonlinear disturbance observer and the fault diagnosis observer is constructed. The novel EADFTC strategy is proposed by integrating composite observer structure with adaptive disturbance observer‐based control theory and H_∞ technology. It is proved that all the signals of closed‐loop system are asymptotically bounded in mean square under the circumstances of multiple heterogeneous disturbances and time‐varying faults occur simultaneously. Finally, the effectiveness and availability of proposed strategy are demonstrated by means of the numerical simulation and a doubly fed induction generators system simulation, respectively.     

Command-filter-based distributed containment control of nonlinear multi-agent systems with actuator failures

In this paper, the problem of distributed containment fault-tolerant control for a class of nonlinear multi-agent systems in strict-feedback form is studied. The considered nonlinear multi-agent systems are subject to unknown nonlinear functions and actuator faults with loss of effectiveness and lock-in-place. By resorting to the universal approximation capability of fuzzy logical systems, the command filtered backstepping technique and nonlinear fault-tolerant control theory, distributed controllers are designed recursively. From the Lyapunov stability theory, it is proved that all signals of the resulting closed-loop systems are cooperatively semi-globally uniformly ultimately bounded and the containment errors converge to a small neighbourhood of origin by properly tuning the design parameters. Finally, a numerical example is provided to show the effectiveness of the proposed control method.     

信息融合技术在大型发电机故障诊断中的应用

研究大型发电机故障诊断问题,大型发电机组故障具有复杂性和多样性,单从某一方面进行故障诊断,诊断结果比较低。为提高了大型发电机故障诊断准确率,提出一种基于信息融合技术的大型发电机故障诊断方法。首先对故障特征进行提取和降维,然后采用多个支持向机对大型发电机组故障进行初步诊断,获得相互独立的证据,最后对各证据采用DS证据理论融合算法进行融合,从而实现对大型发电机故障的准确诊断。仿真结果表明,采用信息融合的故障诊断系统有效地提高了大型发电机故障的诊断精度,增加故障诊断结果的置信度。