Design and Implementation of Urban-rural Power Grid Dispatching Fault Diagnosis Expert System

The paper designs the urban-rural power grid dispatching fault diagnosis expert system which acquires fault information by SCADA system of automatic system of urban-rural power grid, and uses artificial intellegence method to analyze fault information and make fault diagnosis. The paper implements the core part of the fault expert system the design of knowledge base and fault inference engine.

Design and implementation of an expert system for remote fault diagnosis in ship lift

In this paper an expert system for remote fault diagnosis in the ship lift was developed by analysis of the fault tree and combination with VPN. The fault tree was constructed based on the operation condition of the ship lift. The diagnosis model was constructed by hierarchical classification of the fault tree structure, and the inference mechanism was given. Logical structure of the fault diagnosis in the ship lift was proposed. The implementation of the expert system for remote fault diagnosis in the ship lift was discussed, and the expert system developed was realized on the VPN virtual network. The system was applied to the Gaobaozhou ship lift project, and it ran successfully.

Fuzzy Fault Tree Analysis for Fault Diagnosis of Cannula Fault in Power Transformer

Being one of the most expensive components of an electrical power plant, the failures of a power transformer can result in serious power system issues. So fault diagnosis for power transformer is highly important to ensure an uninterrupted power supply. Due to information transmission mistakes as well as arisen errors while processing data in surveying and monitoring state information of transformer, uncertain and incomplete information may be produced. Based on these points, this paper presents an intelligent fault diagnosis method of power transformer using fuzzy fault tree analysis (FTA) and beta distribution for failure possibility estimation. By using the technique we proposed herein, the continuous attribute values are transformed into the fuzzy numbers to give a realistic estimate of failure possibility of a basic event in FTA. Further, it explains a new approach based on Euclidean distance between fuzzy numbers, to rank the basic events in accordance with their Fuzzy Importance Index.

Optimization of a dynamic uncertain causality graph for fault diagnosis in nuclear power plant

Fault diagnostics is important for safe operation of nuclear power plants(NPPs). In recent years, data-driven approaches have been proposed and implemented to tackle the problem, e.g., neural networks, fuzzy and neurofuzzy approaches, support vector machine, K-nearest neighbor classifiers and inference methodologies. Among these methods, dynamic uncertain causality graph(DUCG)has been proved effective in many practical cases. However, the causal graph construction behind the DUCG is complicate and, in many cases, results redundant on the symptoms needed to correctly classify the fault. In this paper, we propose a method to simplify causal graph construction in an automatic way. The method consists in transforming the expert knowledge-based DCUG into a fuzzy decision tree(FDT) by extracting from the DUCG a fuzzy rule base that resumes the used symptoms at the basis of the FDT. Genetic algorithm(GA) is, then, used for the optimization of the FDT, by performing a wrapper search around the FDT: the set of symptoms selected during the iterative search are taken as the best set of symptoms for the diagnosis of the faults that can occur in the system. The effectiveness of the approach is shown with respect to a DUCG model initially built to diagnose 23 faults originally using 262 symptoms of Unit-1 in the Ningde NPP of the China Guangdong Nuclear Power Corporation. The results show that the FDT, with GA-optimized symptoms and diagnosis strategy, can drive the construction of DUCG and lower the computational burden without loss of accuracy in diagnosis.

Fault Diagnosis Based on Graph Theory and Linear Discriminant Principle in Electric Power Network

In this paper, we adopt a novel topological approach to fault diagnosis. In our researches, global information will be introduced into electric power network, we are using mainly BFS of graph theory algorithms and linear discriminant principle to resolve fast and exact analysis of faulty components and faulty sections, and finally accomplish fault diagnosis. The results of BFS and linear discriminant are identical. The main technical contributions and innovations in this paper include, introducing global information into electric power network, developing a novel topological analysis to fault diagnosis. Graph theory algorithms can be used to model many different physical and abstract systems such as transportation and communication networks, models for business administration, political science, and psychology and so on. And the linear discriminant is a procedure used to classify an object into one of several a priori groupings dependent on the individual characteristics of the object. In the study of fault diagnosis in electric power network, graph theory algorithms and linear discriminant technology must also have a good prospect of application.

Intelligent Examination Monitoring and Maintenance for the Safety of Operation of Flexible Manufacturing Systems

Based on the system of electric power supply for flexible manufacturing systems (FMS), a study has been carried out on the intelligent safety examination, monitoring and maintenance of its running environment. On the basis of the specific feature of the power supply network of an FMS, real time monitoring system of the power supply network and the fault diagnostic expert system for the power equipment have been designed. This system can diagnose not only definite fault phenomena, but also fuzzy, uncertain fault phenomena as well. Fault diagnostic knowledge base for the power equipment has been founded hierarchy architecture model and the method of fault tree analysis. Feasibility of this system has been proved by computer simulation.

Overview of condition monitoring and operation control of electric power conversion systems in direct-drive wind turbines under faults

Electric power conversion system （EPCS）, which consists of a generator and power converter, is one of the most important subsystems in a direct-drive wind turbine （DD-WT）. However, this component accounts for the most failures （approximately 60% of the total number） in the entire DD-WT system according to statistical data. To improve the reliability of EPCSs and reduce the operation and maintenance cost of DD-WTs, numerous researchers have studied condition monitoring （CM） and fault diagnostics （FD）. Numerous CM and FD techniques, which have respective advantages and disadvantages, have emerged. This paper provides an overview of the CM, FD, and operation control of EPCSs in DD-WTs under faults. After introducing the functional principle and structure of EPCS, this survey discusses the common failures in wind generators and power converters; briefly reviewed CM and FD methods and operation control of these generators and power converters under faults; and discussed the grid voltage faults related to EPCSs in DD-WTs. These theories and their related technical concepts are systematically discussed. Finally, predicted development trends are presented. The paper provides a valuable reference for developing service quality evaluation methods and fault operation control systems to achieve high-performance and high-intelligence DD-WTs.

Diagnosis Petri Net Model for Power System

This paper puts forward a new fault diagnosis model based on Petri net.In this new fault diagnosis method,an associated tree is f irst defi ned to describe the logic according to the protections and circuit breakers.After that a Petri net diagnosis model comes into being and the precise diagnosis of power grid elements can be realized at the same time.The simulation results given in this paper illustrate that this fault diagnosis method can make a rapid diagnosis in complex fault situation and give accurate results to power grid fault diagnosis.

基于CART决策树的FPSO单点系泊系统电滑环故障诊断

为了保障FPSO单点系泊系统电滑环运行安全,有必要开展电滑环故障诊断方法的研究,为单点系泊系统电滑环故障预警与排查提供依据。本文针对FPSO单点电滑环故障的诊断问题,采用了一种基于CART决策树算法的故障诊断模型;进一步利用有限元制作样本数据集,并通过该数据集训练得到了电滑环故障诊断模型;最后,通过后剪枝法完成了诊断模型的简化,实现了对电滑环的故障诊断。分析结果可以得到,该模型具有较高的准确率和较快的诊断速度,能够对电滑环进行有效的诊断,以便工作人员及时采取措施修复或更换电滑环,保证FPSO的安全运行。

基于故障树和产生式规则的故障诊断专家系统设计

[目的]为充分运用核动力装置的运行管理经验辅助核动力操纵人员进行故障诊断,设计一种船用核动力装置故障诊断专家系统。[方法]首先,根据故障树与产生式规则之间的逻辑一致性,提出一种将故障树知识转化为产生式规则的方法;然后,对采用正、反向混合推理方法的专家系统知识库和推理机进行优化设计,并依据故障树最小割集和重要度分析结果设计正向推理策略以简化推理流程;最后,根据人工对故障状态判断的思路设计状态监测模块,实时采集关键设备参数以转化为专家系统可识别的设备信息。[结果]结果显示,采用所提方法可解决专家系统知识获取困难的问题,能在保证推理准确度的前提下提升推理效率,实现了专家系统的在线故障诊断功能。[结论]研究表明采用所提方法可提升专家系统获取知识的能力和推理效率,对保障核动力装置的运行管理安全具有重要意义。

基于油中溶解气体特征量筛选的变压器故障诊断方法

油中溶解气体分析对变压器故障预警及诊断具有重要意义。针对油中溶解气体特征量种类众多、故障关联特征分析不足等问题,文中以油浸式变压器为研究对象,提出了基于油中溶解气体特征量筛选的变压器故障诊断方法。首先,对油中溶解气体的原始特征量进行特征衍生,通过随机森林(random forest,RF)计算特征量对故障诊断的重要度,筛选得到最佳特征组合。其次,采用树结构概率密度估计(tree-structured parzen estimator,TPE)实现RF模型的参数寻优,并形成TPE-RF诊断模型。同时,结合多种评价指标,证明所提方法能够对变压器作出准确的故障诊断。最后,提出TreeSAHP模型分析特征量对各故障的重要度,优选出各故障关联的主要特征量,并根据变压器运行案例,探讨了该方法在电力行业现场应用中的适用性,验证了该方法的有效性。

多模态马尔科夫决策泛在电力物联网大数据智能挖掘

针对泛在电力物联网结构复杂、数据多样且不确定的问题,提出了一种基于多模态马尔科夫决策的泛在电力物联网大数据智能挖掘方法。该方法构建了一种基于最大熵的马尔科夫决策算法,对电力泛在物联网进行故障诊断和负荷预测,具有标记样本需求量小、置信度高的特点。通过结合电气量信息及开关量信息来提取电网数据特征,从而充分利用多模态数据样本。仿真分析与实验结果表明,相比于传统方法,所提方法能够有效识别出包括信息畸变在内的电网故障,提升电网故障诊断的准确率和电网负荷预测的精度。

飞机电源系统故障诊断方法综述及发展趋势

飞机电源系统是机上一切用电设备的电能来源,其安全性与可靠性至关重要。在环保和高效发展需求的背景下,现代航空工业正在推进以电能为核心的多电/全电飞机技术的研究和应用。电驱动装置和电力电子器件的广泛使用导致飞机电源系统结构的复杂化,对飞机的可靠性、安全性、测试性和维修性提出了更高的要求,研究飞机电源系统的故障诊断技术具有重要意义。本文首先介绍了飞机电源系统的组成结构和各自功能,概述了飞机电源系统的发展历程,对比分析国内外典型电源系统的特征,总结了飞机电源系统中的主要故障模式、故障特点和失效原因,并提出一种飞机电源健康管理系统的设计架构,然后综述了国内外基于模型和基于数据的故障诊断方法研究进展,从准确度、数据需求量、适用性和实现难易程度等方面评述了各类诊断方法的特点,最后指出了飞机电源系统故障诊断技术面临的挑战和发展趋势。

高速公路机电设备健康状态物联监测与智能诊断

针对高速公路机电设备运维管理存在健康状态数据不能实时获取、故障发现不够及时准确、设备维护处理效率较低等问题,本文提出应用物联网和智能诊断模型处理技术,构建一种远程机电设备健康状态物联监测与智能诊断系统。该系统通过实时监测设备基础状态数据,利用物联网技术将数据传输到云处理平台,对数据进行分析、处理、判断机电设备运行的健康状态及故障智能诊断。实际运行结果表明:该系统可准确地判断外场机电设备的健康状态,减少现场维护频次约15%,缩短运维和巡检车辆行驶里程约30%,实现了节能减排,降低了运维成本,缓解了高速公路拥堵,进而取得良好的经济和社会效益。

基于改进决策树算法的电能计量装置故障自动化诊断系统

电网中电能计量装置具有周检工作量大、数量多、巡检成本较高、巡检效率较低以及故障定位用时较长等问题,该文提出以改进决策树算法为依据的电能计量装置故障自动化诊断系统。设计相应的电能计量装置在线监测流程,结合数据挖掘以及决策树算法进行算例模型的搭建。经过实验验证,所提系统能够更高效地进行故障诊断,同时其自动化诊断故障的准确率相比人工判读更高。

基于故障树分析识别核动力厂电气系统火灾共模故障

本研究在工程实践的基础上,建立了一种基于故障树分析识别电气系统火灾共模故障的方法。通过建立故障树,并采用逻辑代数进行计算分析,识别出电气系统的火灾共模故障。本研究能够系统性地识别出电气系统的火灾共模故障,减少主观判断,提高识别结果的准确性,可以广泛地应用于核动力厂的火灾安全分析和防火安全设计。

基于门控循环网络的故障诊断方法研究

核动力装置具有内反馈和强耦合的特征,使得内部工况环境复杂,一旦出现异常则更有可能发生高风险事故。为了提高核动力装置运行的安全性,论文提出了一种基于门控循环网络的故障诊断方法,该方法利用核动力装置运行过程中产生的数据进行瞬态特征与时变特征的提取,通过梯度提升树算法和门控循环网络算法进行联合诊断。实验结果表明,与其他模型相比,该模型能够更加准确地判断每一个故障,故障诊断的准确率达到99.9%以上。

基于二叉故障树的某船用特种设备故障诊断

旨在解决某船用特种设备在补给物资转运过程中出现故障时进行诊断并给出相应故障解决方案的问题。结合设备结构组成及运行工况特点,利用故障树模型,从设备故障实例出发,将设备各个故障事件之间的逻辑关系通过故障树的形式表达出来,将构建的故障树模型转化为二叉故障树模型,建立基于二叉故障树的故障诊断专家系统,利用专家系统进行设备的故障诊断。结果表明该专家系统知识库内的知识存储结构形式简单,推理机逻辑清晰,专家系统容易实现和操作,且可维护性也较好,能够对某船用特种设备进行故障诊断。将基于二叉树的故障诊断专家系统应用于某船用特种设备的故障诊断,可快速查明设备在运行过程中发生的各种故障原因并进行故障的快速解决,有效提高了设备的故障诊断效率,并对补给物资的转运过程提供了重要保障。

大数据下电动汽车动力电池故障诊断技术面临的挑战及其发展

随着绿色理念的持续推行,新能源电池在汽车行业逐渐成为新型动力,促使电动汽车不断创新发展。但随着电动汽车的推广和普及,其动力电池故障问题明显增多,通过有效采用智能化的诊断技术,有助于及时解决电池故障,确保行车安全。鉴于此,解决大数据时代背景下,探究电动汽车动力电池故障诊断技术至关重要,通过结合相关文献资料和实践工作,深入分析电动汽车动力电池故障的常见类型与特点,探究电动汽车动力电池故障诊断技术面临的挑战,并提出相应的解决措施,以推动动力电池故障诊断技术的有效应用,最后对其未来发展进行展望,以推动电动汽车行业健康发展。

股道管理自动化故障诊断系统设计及应用

股道管理自动化系统能够完成站场内所有信号设备的控制,涵盖整备场股道、联锁信号、道岔控制、车辆监控等内容,倘若其在运行过程中系统联锁出现问题,不能及时进行修复,将会影响铁路工作的运输效率,甚至造成重大的灾难性事故。因此,故障诊断及处理的实时性是该系统运维过程中最重要的一环。为提高该系统长期稳定运行,提出一种基于故障树分析结果研发的股道管理自动化故障诊断系统,首先对系统结构进行分析,了解信号机、轨道电路、道岔的工作流程;然后利用故障树分析法对股道管理自动化系统建立故障树,逐层分析故障原因,列出所有底事件,进一步对故障进行定性定量分析,从中找出系统的薄弱环节,制定故障解决策略方案;最后依据故障树结果开发出故障诊断系统软件。通过测试,股道自动化系统故障诊断软件故障筛查及确认度可达100%,故障处理时间提高75%,故障处理人员减少70%。实践证明,故障诊断系统为股道管理自动化系统的检修、运维、故障诊断提供理论基础及指导,实现该系统在运维中故障的快速识别与诊断分析。