Fault Diagnosis Model Based on Fuzzy Support Vector Machine Combined with Weighted Fuzzy Clustering

A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC).Considering the relationship between the sample point and non-self class,FC algorithm is applied to generate fuzzy memberships.In the algorithm,sample weights based on a distribution density function of data point and genetic algorithm (GA) are introduced to enhance the performance of FC.Then a multi-class FSVM with radial basis function kernel is established according to directed acyclic graph algorithm,the penalty factor and kernel parameter of which are optimized by GA.Finally,the model is executed for multi-class fault diagnosis of rolling element bearings.The results show that the presented model achieves high performances both in identifying fault types and fault degrees.The performance comparisons of the presented model with SVM and distance-based FSVM for noisy case demonstrate the capacity of dealing with noise and generalization.

FAULT DIAGNOSIS EXPERT SYSTEM FOR ROTATING MACHINERY BASED ON A FUZZY PROBABILITY LOGIC INFERENCE MODEL

A new theory- the fuzzy probability logic theory is presented , This theory incorpo- rates the genterally-used fuzzy logic and the traditionally-used probability logic theory in attempt to emulate the rational fault diagnosis under uncertainty. According to the theory , an inference model , named as FSL , is thus designed to be devoted to the building of a fault diagnosis expert system for rotating machinery (ROSLES) . The system is put into operation on a vibration simula- tor stand for 300 MW turbine generator set ( 1 : 1 0) and satisfactory results are gained.

Fault detection and identification for dead reckoning system of mobile robot based on fuzzy logic particle filter

To deal with fault detection and diagnosis with incomplete model for dead reckoning system of mobile robot,an integrative framework of particle filter detection and fuzzy logic diagnosis was devised.Firstly,an adaptive fault space is designed for recognizing both known faults and unknown faults,in corresponding modes of modeled and model-free.Secondly,the particle filter is utilized to diagnose the modeled faults and detect model-free fault according to the low particle weight and reliability.Especially,the proposed fuzzy logic diagnosis can further analyze model-free modes and identify some soft faults in unknown fault space.The MORCS-1 experimental results show that the fuzzy diagnosis particle filter(FDPF) combinational framework improves fault detection and identification completeness.Specifically speaking,FDPF is feasible to diagnose the modeled faults in known space.Furthermore,the types of model-free soft faults can also be further identified and diagnosed in unknown fault space.

Power transformer fault diagnosis model based on rough set theory with fuzzy representation

Objective Due to the incompleteness and complexity of fault diagnosis for power transformers,a comprehensive rough-fuzzy scheme for solving fault diagnosis problems is presented.Fuzzy set theory is used both for representation of incipient faults' indications and producing a fuzzy granulation of the feature space.Rough set theory is used to obtain dependency rules that model indicative regions in the granulated feature space.The fuzzy membership functions corresponding to the indicative regions,modelled by rules,are stored as cases.Results Diagnostic conclusions are made using a similarity measure based on these membership functions.Each case involves only a reduced number of relevant features making this scheme suitable for fault diagnosis.Conclusion Superiority of this method in terms of classification accuracy and case generation is demonstrated.

基于改进的方向性加权模糊Petri网配电网故障诊断

为提高配电网保护动作信息丢失时故障诊断的准确率及容错性,提出了基于改进的方向性加权模糊Petri网配电网故障诊断方法。首先,考虑故障发生时近后备保护对可疑故障元件故障诊断的影响,对可疑故障元件的各个故障蔓延方向进行建模;其次,在专家经验的基础上采用随机数对模型输入权值进行随机赋值;最后,在保护动作信息丢失时,跟踪改变可疑故障元件保护未动作的可信度取值,并分析该故障诊断方法的适应性与容错性。仿真结果表明,所提基于改进的方向性加权模糊Petri网配电网故障诊断模型在信息不完备或者保护及断路器拒动时,均具有较高的故障诊断准确率。

化工过程故障诊断的模糊概率Petri-net的研究

本文研究基于模糊与概率信息的模糊概率Petri net故障的诊断方法 ,概率信息用来解决问题冲突 ,模糊信息用来解决故障诊断不确定性并计算诊断的可靠性。在石油化工装置中的应用表明 。

基于单片机和Fuzzy技术的液压在线故障监测系统的设计

介绍了基于单片机和Fuzzy技术的液压在线故障监测诊断系统的设计,给出了系统组成和软件设计。该系统以高性能单片机为核心,采用Fuzzy逻辑分析技术实现了液压系统故障诊断的基本结构和知识库的方法,建立了液压系统故障Fuzzy分析的逻辑结构,实验表明,该系统提高了液压系统故障推理的正确性和推理速度。

基于物联网的加权模糊petri网电网故障诊断研究

为了解决电力SCADA系统监测数据存在漏报误报等情况造成故障诊断结果不可靠的问题,文章提出基于物联网的加权模糊petri网电网故障诊断方法。首先,考虑保护与断路器动作的逻辑顺序,对权值进行合理的设定;其次,对电网拓扑结构进行分析,建立故障元件按方向蔓延的加权模糊petri网模型;最后,将故障诊断结果与传感器网络监测到的温度数据相结合,分析故障诊断结果可信度的可靠性。仿真结果表明,所提出的基于物联网的加权模糊petri网电网故障诊断方法在监测中心获取信息不完备的情况下,仍可以得出正确的诊断结果,具有较高的适应性和容错性。

Intelligent Fault Diagnosis in Lead-zinc Smelting Process

According to the fault characteristic of the imperial smelting process （ISP）, a novel intelligent integrated fault diagnostic system is developed. In the system fuzzy neural networks are utilized to extract fault symptom and expert system is employed for effective fault diagnosis of the process. Furthermore, fuzzy abductive inference is introduced to diagnose multiple faults. Feasibility of the proposed system is demonstrated through a pilot plant case study.

Intelligent Process Fault Diagnosis for Nonlinear Systems with Uncertain Plant Model via Extended State Observer and Soft Computing

There have been many studies on observer-based fault detection and isolation (FDI), such as using unknown input observer and generalized observer. Most of them require a nominal mathematical model of the system. Unlike sensor faults, actuator faults and process faults greatly affect the system dynamics. This paper presents a new process fault diagnosis technique without exact knowledge of the plant model via Extended State Observer (ESO) and soft computing. The ESO’s augmented or extended state is used to compute the system dynamics in real time, thereby provides foundation for real-time process fault detection. Based on the input and output data, the ESO identifies the un-modeled or incorrectly modeled dynamics combined with unknown external disturbances in real time and provides vital information for detecting faults with only partial information of the plant, which cannot be easily accomplished with any existing methods. Another advantage of the ESO is its simplicity in tuning only a single parameter. Without the knowledge of the exact plant model, fuzzy inference was developed to isolate faults. A strongly coupled three-tank nonlinear dynamic system was chosen as a case study. In a typical dynamic system, a process fault such as pipe blockage is likely incipient, which requires degree of fault identification at all time. Neural networks were trained to identify faults and also instantly determine degree of fault. The simulation results indicate that the proposed FDI technique effectively detected and isolated faults and also accurately determine the degree of fault. Soft computing (i.e. fuzzy logic and neural networks) makes fault diagnosis intelligent and fast because it provides intuitive logic to the system and real-time input-output mapping.

基于加权模糊的时序约束Petri网电网故障诊断方法

电网发生故障后充分利用报警信息对故障元件进行判别,是保障电力系统安全运行的重要环节。针对现有Petri网时序推理与置信度推理相分开、计算过程复杂且未充分利用时间信息等问题,提出了一种基于加权模糊的时序约束Petri网电网故障诊断方法。将时序约束引入到Petri网的库所与变迁中,根据保护与断路器之间的动作顺序关系建立加权模糊的时序约束Petri网模型。定义置信度与时间区间分层推理运算,实现置信度推理与时序推理同步进行,在置信度推理过程中充分考虑到时间信息的影响。通过对多组算例仿真比较得出,所提方法能够有效判别出故障元件,具有较高的准确性和容错性。

基于多源信息的延时约束加权模糊Petri网故障诊断模型

提出一种基于电气量判据、保护判据和断路器判据,并能够计及这3类判据时序属性的多源信息延时约束加权模糊Petri网故障诊断模型。首先,分析故障前后的电力网络拓扑以确定可疑故障元件。之后,采用反向推理寻径法识别故障元件动作可能路径,并在此基础上构造加权模糊Petri网故障诊断模型来确定故障元件;该模型能够计及故障电气量及其时序属性、元件故障和保护动作之间、保护动作和断路器跳闸之间的延时约束以及保护和断路器动作不确定性,且诊断推理全程采用矩阵运算来实现。在上述基础上,采用逆向推理判断警报信息的完整性和正确性,识别出误动或拒动的保护与断路器。最后,用IEEE 39节点系统和贵州兴仁变电站实际故障案例对所构建的故障诊断模型的正确性和求解方法的有效性作了验证。

基于方向性加权模糊Petri网的电网故障诊断方法

针对复杂电网故障诊断的模糊Petri网模型不能适应网络拓扑变化且未考虑保护和断路器信息对故障影响程度等问题,提出了基于方向性加权模糊Petri网的电网故障诊断方法。首先通过电力网络拓扑分析确定停电区域;其次,分别在每个故障蔓延方向上对停电区域内的元件建模进行推理计算;最后分析该算法在网络拓扑变化时的通用性以及信息不完备情况下的容错性。对14节点电力系统的仿真结果表明:该算法可以有效地降低输入输出矩阵的维数,减小计算量,能够自动适应网络拓扑结构的变化,在信息不完备的情况下给出正确的诊断结果,具有较好的通用性和容错性。

基于粗糙集理论与模糊Petri网络的油浸电力变压器综合故障诊断

电力变压器故障诊断因果关系的复杂性与模糊性,采用单一智能方法难以准确描述。文中基于智能互补融合的思想,将粗糙集理论与模糊Petri网络有机结合在一起进行油浸电力变压器故障诊断。利用粗糙集信息表简化技术来实现对专家知识的简化与故障特征的压缩,获得最小诊断规则,基于最小诊断规则的Petri网络模型可以有效降低网络结构的复杂性与故障特征获取的难度。同时利用模糊Petri网络实现并行模糊推理,便于描述故障特征的变化及对变压器运行特性的快速分析。故障实例分析表明,文中所提出的智能方法可以有效地进行模糊推理,减小诊断信息的冗余性,诊断效率高,计算快速、准确,结果易于被人理解。

基于BP网络算法优化模糊Petri网的电力变压器故障诊断

为了提高电力变压器故障诊断的正确率,提出了一种基于BP网络算法优化模糊Petri网的电力变压器故障诊断方法。利用具有自学习、自适应能力的BP网络算法,在确定模糊Petri网的权值、阈值、可信度等网络参数初始值的前提下,实现模糊Petri网网络参数的优化。在模糊Petri网网络结构上,运用BP网络算法,对电力变压器DGA样本进行学习训练,使模糊Petri网网络参数逐步向真实值逼近。实例分析结果表明,该方法能够有效地诊断电力变压器中的单一故障和多重故障,提高故障诊断正确率,证明了方法的正确性和有效性。

基于时序信息的模糊Petri网电网故障诊断方法

为提高电网故障诊断的准确性,充分利用报警信号的时序信息,研究一种基于时序信息的分层变迁模糊Petri网故障诊断方法。首先,建立分层变迁加权模糊Petri网(WFPN)故障诊断模型;其次,构建元件、保护和断路器之间的时间关联特性(TAC),进行时间关联一致性检查;最后,通过模糊推理进行故障诊断,并对保护和断路器异常行为进行评判。通过典型电网算例推理验证,结果证明该方法不仅能够提高故障诊断的准确性和容错性,还能够判断保护和断路器的异常行为并推出元件故障发生时刻。

基于模糊时间Petri网的电网故障诊断方法

故障元件判别作为电网故障应急处置的首要环节,是在线调控运行的重点工作之一。针对现有基于Petri网的电网故障诊断方法未充分利用时序信息或者时序推理过程复杂的问题,提出一种基于模糊时间Petri网的电网故障诊断方法。为Petri网中库所及变迁引入时间属性以表征电力系统告警信息的时序约束关系,定义了置信概率与时序约束的关联推理运算,并从模型结构出发建立了模糊时间Petri网的分层推理过程,能够同时推理得到元件故障的置信概率及其时间点约束。算例仿真表明,所提方法能够快速判定故障元件,诊断结果正确且合理,具有较强的容错性,且能对告警信息做出正确评价。

模糊PETRI网在电力系统故障诊断中的应用

在电力系统中,如何处理大量不完全且不确定的继电保护及断路器动作信息,从而准确地进行故障诊断,一直是研究人员所关心的问题。该文利用具有较好的并行处理性、快速性及容错性的模糊Petri网来处理电力系统故障诊断中的不确定动作信息,给出了一种新的故障诊断分析方法,可实现准确而有效的故障诊断功能。文中首先介绍了模糊Petri网的结构和定义;然后建立了故障诊断的模糊Petri网模型;最后通过实例数据验证了此方法的有效性和可行性。

基于模糊神经Petri网的故障诊断模型

Petri网是对具有产生式规则的故障诊断系统的有力建模工具,但其缺乏较强的学习能力.本文以Petri网的基本定义为基础,结合模糊逻辑和Petri网模型,定义了模糊Petri网模型,在此基础上引入人工神经网络技术,给出了人工神经网络的模糊Petri网表示方法,并针对工程机械故障诊断异步、离散等特点,提出并建立了故障诊断的模糊神经Petri网模型及其改进模型.基于模糊神经Petri网的故障诊断系统结合了Petri网和人工神经网络的优点,经过自学习后同时具有很强的推理能力和自适应能力.

基于改进动态自适应模糊Petri网与BP算法的电网故障诊断

为减小电网故障诊断过程中人为主观因素造成的误差,提高模糊Petri网模型的容错性和准确性,提出基于改进动态自适应模糊Petri网与BP算法的电网故障诊断方法。首先,在动态自适应模糊Petri网中引入补充弧元组,并利用动态自适应模糊Petri网能动态适应专家系统中模糊知识更新的特性,构建电网故障诊断的通用Petri网模型;其次,利用神经网络中的BP算法对模型中的参数进行训练;最后,分析该故障诊断方法的适应性与容错性。对某8节点测试系统和吉林四平实际电网的仿真结果表明:该算法充分利用了Petri网的并行处理能力,推理简单且思路清晰,在信息不完备的情况下能给出较准确的诊断结果,具有较好的容错性。