大型变压器状态监测与故障诊断技术的研究

大型变压器是所有供电系统中进行调节电压并实现电能分配的重要设施,大型变压器能否稳定的运行也直接决定了整个电网运行的基本质量,因此,为了能够保证大型变压器安全稳定的运行需要相关技术部门能够对大型变压器进行状态检测,及时做好故障诊断。本文主要讲述了大型变压器具体状态监测以及故障诊断技术。     

基于LabVIEW的变压器在线状态监测系统

变压器的在线状态监测对于变压器的故障诊断以及电网的正常运行具有重要的意义。对以机械参数为主、电气参数为辅的变压器振动分析法进行了研究,建立了一套在线状态监测系统。系统通过工作现场的数据采集终端检测变压器的振动、电压、电流和油温信号,使用串口和以太网实现数据采集终端和上位机之间的数据通信;上位机采用LabVIEW软件对这些信号进行分析,从而判断变压器的工作状态。试验表明,系统具有良好的在线监测效果。     

变压器运行状态检测及故障诊断

变压器是电网运行中的重要设备,也是所有电力网络设备中较容易发生故障的环节,它能否安全运行是电网能否安全运行的基础。所以对变压器的运行状态进行检测并能够根据检测结果及时排除故障,已成为一项具有理论和实用双重价值的研究方向。本文通过对变压器的运行状态检测方法及手段进行分析,采用贝叶斯和决策树对故障类型进行诊断,具有一定的实用意义。     

变压器绕组状态的振动在线监测

提出了运用振动法在线监测变压器绕组的状态来实现变压器绕组故障预警和故障定位的方法.在对振动法测量原理研究的基础上,分析了变压器绕组在变压器运行时的振动与所受电磁力的关系.根据分析所得的振动信号特征,设计了变压器绕组状态的振动监测系统.最后利用该系统对实际的变压器进行相关的振动监测实验,实验结果验证了不同故障时对应的振动信号的特性不同,从而为最终实现变压器绕组故障诊断奠定了良好的基础.     

便携式变压器振动监测与故障诊断系统设计

设计一种基于嵌入式Windows系统的便携式变压器振动监测与故障诊断系统。介绍系统设计与实现的关键技术,包括信号采集、信号调理、A/D转换等硬件模块以及数据库、故障诊断算法、用户界面等软件模块的开发过程。该系统通过采集变压器的振动、电流、电压等信号对变压器状态及其故障进行分析与诊断,使用多种分析诊断算法,包括谱分析、幅值分析等常规方法,以及周期性诊断算法、模型诊断算法等新的分析诊断方法,用于及时地发现变压器的异常并发出警报。应用结果表明,该系统能够方便、有效地实现变压器运行状态及其故障的带电监测。     

汽车动力传动系统的状态监测与故障诊断

本文以汽车动力传动系统为对象，综合振动分析，铁谱、光谱分析，理化分析和工程数据库等多种手段，建立了基于数据融合与神经网络技术的故障诊断专家系统。     

浅析电气设备状态监测与故障诊断技术应用

随着经济的发展,人们的生活质量越来越高,对电力的需求也逐渐增加,一旦电力系统出现故障,而不能正常供电,将会给人们的生产生活造成严重的影响。所以,必须做好工作,使整个电力运行系统的安全性和可靠性得到有效的保障,以此来确保电的有效供给量,使人们的生产生活得以正常运行。     

车载状态监测与故障诊断专家系统设计

综合应用计算机软件硬件技术,实现对机车主要质量状态参数的实时采集、显示和记录,并进行实时机车故障诊断、语音定点定性报警提示,通过转储分析,获取机车性能变化趋势和故障先兆信息,及时发现故障隐患,提高机车质量,确保行车安全。     

Modeling a web-based remote monitoring and fault diagnosis system with UML and component technology

As the Machinery Condition Monitoring and Fault Diagnosis Systems (MCMFDSs) are more and more complex, the design and development of these systems are becoming a challenge. The best way to manage the complexity and risk is to abstract and model them. This paper presents a new method of modeling Web-based Remote Monitoring and Fault Diagnosis Systems (WRMFDSs) with Unified Modeling Language (UML). A component framework model is put forward. A highly maintainable WRMFDS with three reusable component packages was developed using component-based programming. This paper, which studies a reusable WRMFDS model, aims at making such advanced information technologies be used widely in the condition monitoring and fault diagnosis domain, it can give developers a paradigm to accomplish the similar systems.     

Investigation of engine fault diagnosis using discrete wavelet transform and neural network

An investigation of a fault diagnostic technique for internal combustion engines using discrete wavelet transform (DWT) and neural network is presented in this paper. Generally, sound emission signal serves as a promising alternative to the condition monitoring and fault diagnosis in rotating machinery when the vibration signal is not available. Most of the conventional fault diagnosis techniques using sound emission and vibration signals are based on analyzing the signal amplitude in the time or frequency domain. Meanwhile, the continuous wavelet transform (CWT) technique was developed for obtaining both time-domain and frequency-domain information. Unfortunately, the CWT technique is often operated over a longer computing time. In the present study, a DWT technique which is combined with a feature selection of energy spectrum and fault classification using neural network for analyzing fault signal is proposed for improving the shortcomings without losing its original property. The features of the sound emission signal at different resolution levels are extracted by multi-resolution analysis and Parseval’s theorem [Gaing, Z. L. (2004). Wavelet-based neural network for power disturbance recognition and classification. IEEE Transactions on Power Delivery 19, 1560–1568]. The algorithm is obtained from previous work by Daubechies [Daubechies, I. (1988). Orthonormal bases of compactly supported wavelets. Communication on Pure and Applied Mathematics 41, 909–996.], the“db4”, “db8” and “db20” wavelet functions are adopted to perform the proposed DWT technique. Then, these features are used for fault recognition using a neural network. The experimental results indicated that the proposed system using the sound emission signal is effective and can be used for fault diagnosis of various engine operating conditions.     

Conceptual design of remote monitoring and fault diagnosis systems

Condition monitoring and fault diagnosis are of fundamental importance for many industrial systems. In the last decade, substantial research efforts have been made on the surveillance and diagnosis systems for different types of equipment, with the approach of integrating information technologies and intelligent computing methods. This paper presents the conceptual design of a distributed information system of condition monitoring and fault diagnosis for a growing number of gas turbine-based power generation systems. Each individual information system that monitors a specific gas turbine system, locally deployed in a power plant, is linked to another information system, deployed at the manufacturer's site, which oversees all the gas turbine systems in parallel. The systems are constructed on the basis of COM components, which are conceptually separated into three tiers. Subsequently, this paper proceeds to present a generic business domain model with components encapsulating physical entities of interest. Finally, this paper addresses the interactions among components, which considerably affect the performance of the system including efficiency and effectiveness. It has been identified that both asynchronous and synchronous communication mechanisms are required for exchanging information for different scenarios. COM+ services are also required for supporting object pooling, transaction coordination, and security control.     

A comprehensive fault diagnosis technique for reversible logic circuits

Fault diagnosis is a complex and challenging problem in reversible logic circuits. The paper proposes a novel fault diagnosis technique for missing control faults in reversible logic circuits. The main focus of this technique is to extract the unique fault signature for each missing control fault in the circuit. The fault signatures are the sequences of test vectors to identify the location of the faults. Based on these fault signatures a unique fault diagnosis tree is built. Our proposed fault diagnosis algorithm is used to traverse the fault diagnosis tree to find the presence and location of the fault. The traversal process is simple and fast. The algorithm executes in linear time and experimental results for benchmark circuits show the reduction of test patterns compared to earlier works.     

Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network

The faults in switched reluctance motors （SRMs） were detected and diagnosed in real time with the Kohonen neural network. When a fault happens, both financial losses and undesired situations may occur. For these reasons, it is important to detect the incipient faults of SRMs and to diagnose which faults have occurred. In this study, a test rig was realized to determine the healthy and faulty conditions of SRMs. A data set for the Kohonen neural network was created with implemented measurements. A graphical user interface （GUI） was created in Matlab to test the performance of the Kohonen artificial neural network in real time. The data of the SRM was transferred to this software with a data acquisition card. The condition of the motor was monitored by marking the data measured in real time on the weight position graph of the Kohonen neural network. This test rig is capable of real-time monitoring of the condition of SRMs, which are used with intermittent or continuous operation, and is capable of de- tecting and diagnosing the faults that may occur in the motor. The Kohonen neural network used for detection and diagnosis of faults of the SRM in real time with Matlab GUI was embedded in an STM32 processor. A prototype with the STM32 processor was developed to detect and diagnose the faults of SRMs independent of computers.     

A method for condition monitoring and fault diagnosis in electromechanical system

Condition monitoring of electrical machines has received considerable attention in recent years. Many monitoring techniques have been proposed for electrical machine fault detection and localization. In this paper, the feasibility of using a nonlinear feature extraction method noted as Kernel independent component analysis (KICA) is studied and it is applied in self-organizing map to classify the faults of induction motor. In nonlinear feature extraction, we employed independent component analysis (ICA) procedure and adopted the kernel trick to nonlinearly map the Gaussian chirplet distributions into a feature space. First, the adaptive Gaussian chirplet distributions are mapped into an implicit feature space by the kernel trick, and then ICA is performed to extract nonlinear independent components of the Gaussian chirplet distributions. A thorough laboratory study shows that the diagnostic methods provide accurate diagnosis, high sensitivity with respect to faults, and good diagnostic resolution.     

Verbal protocol accuracy in fault diagnosis

The study examined whether concurrent and retrospective verbal protocols possess the potential to provide a non-reactive and valid account of the cognitive processes involved in fault diagnosis. With this goal in mind, a group of subjects performed a fault diagnosis task under concurrent and retrospective verbalization and in a silent control condition as well. In the task, concurrent verbalization led to a considerable increase in time to completion, but exerted no effect on overall accuracy and the adopted strategy. Retrospective verbalization did not have any effect on performance. The strategy-related data obtained under concurrent verbalization proved to be more valid than those obtained under retrospective verbalization. On the basis of die results it is suggested that, where possible, concurrent verbal reports should be collected when trying to get a better understanding of the nature of the fault diagnostic process.     

基于改进MPLS的间歇过程监测与故障诊断

针对间歇过程三维数据预处理中不同展开方式的多向偏最小二乘(MPLS)方法在线应用时存在的缺陷,提出改进的MPLS方法。该方法结合传统沿变量展开与批次展开的优势,不仅包含了批次间的信息,在一定程度上去除了过程的非线性及动态性,而且解决了在线应用时数据填充的问题;其次,该方法采用随时间更新的协方差代替固定的主元协方差充分考虑了得分向量的动态特性:最后,引进时变贡献图的故障诊断方法,实现了对故障源的实时跟踪。将该方法应用到工业青霉素发酵过程中,并与传统的MPLS方法进行比较。结果表明:该方法具有更好的监控性能,并能够及时检测故障及跟踪故障源。