基于神经网络与模糊融合的外围故障诊断方法

针对用传统检测方法诊断模拟电路系统设备外围故障困难的问题,提出了一种利用BP神经网络与模糊融合相结合的故障诊断新方法,将神经网络与模糊融合结合起来,实现两者优势互补;首先利用神经网络的泛化能力对系统内部各可测点电压各用一个独立的BP神经网络对系统进行初级诊断,然后根据初级诊断结果,运用模糊融合诊断方法进行故障诊断,诊断结果更趋于合理,对模拟电路系统的外围故障实现正确定位;该方法能充分利用系统内部故障信息,有效避免采集外围设备信息的困难。     

模糊免疫算法及其在模拟电路故障诊断中的应用

对模糊免疫算法应用于模拟电路故障诊断进行了研究;首先简要介绍了免疫系统的工作机理及一些基本概念,然后在此基础上构建出一种模糊免疫算法,并将免疫算法和模糊聚类法结合起来进行故障诊断;人工免疫算法起到学习样本的作用,以寻找到各样本组的聚类中心;而模糊聚类算法则准确地完成对样本的分类任务;仿真实例表明:立足于模拟电路故障诊断字典法,该算法对模拟电路故障诊断非常有效。     

基于多传感器的发动机故障诊断模糊专家系统

本文介绍了采用多传感器综合监测技术的汽车发动机故障诊断专家系统。总结了汽车发动机常见故障,建立了汽车发动机典型故障集。介绍了汽车发动机的故障征兆提取方法,建立了汽车发动机故障征兆集。提出了一种基于模糊规则汽车发动机故障诊断方法。设计了基于模糊规则的发动机故障诊断专家系统,并通过实例证明了该诊断系统的有效性。     

变压器故障诊断方法的研究

变压器的运行状况直接关系到整个电力系统的安全稳定运行,有效对变压器进行故障诊断具有重要的实际意义。电力变压器油中溶解气体分析（Dissolved Gas Analysis, DGA）已经成为油浸式变压器故障诊断的一种有效支持数据,本文在利用DGA数据的基础上,首先总结了常规IEC比值法的优缺点,并针对其边界问题总结了几种有效改进方法。其次,本文总结了人工神经网络,支持向量机,粗糙集,模糊数学、极限学习机、贝叶斯网络、聚类、人工免疫和petri网络等9种智能算法在变压器故障诊断中的运用,针对其固有问题总结了各自的优化方法。最后,本文介绍了以证据理论为主的综合诊断方法,分析了它优于单一智能算法的方面,并介绍了一些其他方法在变压器故障诊断中的应用。最终得出结论,相比于单一智能方法,信息融合的综合诊断办法能更好地对变压器故障进行诊断。     

神经网络和模糊系统在故障诊断中的应用

本文提出了一种神经网络和模糊系统相结合的分级式故障诊断方法。神经网络通过对部分测量数据的处理，实现系统的回路级故障诊断，输出各回路故障出现的可信度。模糊系统通过对神经网络得到的初步诊断结果和其他测量值的处理，实现系统的元件级故障诊断，并对最终诊断结果作出解释。该方法融合了神经网络自适应学习能力强和模糊系统知识表达明确的优点，简化了神经网络学习数据获取及模糊推理规则建立的过程。通过对热硝酸冷却系统故障诊断的仿真，证明了该故障诊断方法的有效性。     

基于模糊自组织映射神经网络的故障诊断方法

在研究Kohonen自组织映射网络理论的基础上运用模糊理论方法建立了刹车系统模糊故障诊断模型。该模型只需选择足够的具有代表性的故障样本训练神经网络，将代表故障的信息输入给训练好的神经网络，根据神经网络的输出结果，就可以判断发生故障的类型。该模型除能识别已训练过的故障，还能识别未训练过的故障，并且聚类能力强、速度快，因此很符合复杂系统的故障诊断。     

一种基于模糊神经网络的模拟电路故障诊断方法

提出了一种采用小波分析与遗传算法相结合的模糊神经网络对模拟电路进行故障诊断的新方法。该方法采用基于小波分析的主成分分析方法对网络的训练样本进行预处理,提取优化向量后利用遗传算法对模糊神经网络进行训练。对两个模拟电路的诊断实例表明该方法故障覆盖率高,并能有效诊断出同类方法误诊的故障类型。     

基于改进图卷积神经网络的配网线路故障诊断

随着电网的不断扩容,系统结构越来越复杂,多故障频发,而多故障是故障诊断的关键和难点。为解决故障处理数据量大,需要快速、准确地诊断电网故障的问题,本文提出了一种基于模糊优化图卷积神经网络的配网故障诊断模型。首先处理采集的配网故障线路的特征数据;其次,搭建基于图卷积神经网络的故障诊断模型,利用模糊理论建立配电网故障的隶属函数;最后利用训练好的模型进行配网故障诊断。仿真结果表明,模糊优化图卷积神经网络对多故障诊断的准确率高于卷积神经网络以及其他方法,本文方法做出的诊断结果更加精确,综合诊断效果最好。     

Model-based fault diagnosis using fuzzy matching

A new fuzzy-model-based approach to fault detection and diagnosis is proposed. The scheme uses a set of fuzzy reference models which describe faulty and fault-free operation, and a classifier based on fuzzy matching for fault diagnosis. The reference models are obtained off-line from simulation data. A fuzzy model which describes the actual behavior of the plant is identified online from normal operating data and compared to each of the reference models. A degree of similarity is evaluated every time the online fuzzy model is identified. Dempster's rule of combination is used to combine new evidence with that already collected. The method of diagnosis accounts for any ambiguity (which may result from fault-free and faulty operation, or different faults, having similar symptoms at a given operating point) by comparing the fuzzy reference models with each other. Results are presented which demonstrate the effectiveness of the scheme when it is used to detect and identify faults in the cooling coil subsystem of the air-handling unit of both simulated and experimental air-conditioning plant     

Fault diagnosis with multivariate statistical models part I: using steady state fault signatures

Multivariate statistical approaches to fault detection based on historical operating data have been found to be useful with processes having a large number of measured variables and when causal models are unavailable. For fault isolation or diagnosis they have been less powerful because of the non-causal nature of the data on which they are based. To improve the fault isolation with these methods, additional data on past faults have been used to supplement the models. A critical review of this fault isolation literature is given, and an improved approach capable of handling both simple and complex faults is presented. This approach extracts fault signatures that are vectors of movement of the fault in both the model space and the residual space. The directions of these vectors are then compared to the corresponding vector directions of known faults in the fault library. Isolation is then based on a joint plot of the angles between the vectors of the current fault and those of the known faults. Although the fault signatures are based on steady-state information, the methodology assumes that time varying disturbances due to common-cause sources are always present, and it is applied to dynamic data as soon as a fault is detected. The method is demonstrated using a simulated CSTR system with feedback control, and is shown to be effective in isolating both simple and complex faults.     

基于模糊Petri网的无线顺序控制系统的故障诊断

通过分析基于无线传感器网络的顺序控制系统,给出它的故障传播规则。针对模糊Petri网在故障诊断中的置信度模糊推理算法的不足,进行了添加阈值判断的改进。运用改进后的模糊Petri网推理算法对无线顺序控制系统进行故障诊断,计算控制器故障发生的概率,得出其中控制逻辑重新发送概率最大,理论结果与现场实际测试结果基本一致。     

一种基于模糊逻辑的非线性系统故障检测与定位的方法

针对一般非线性系统,提出了一种带故障标志的系统故障模糊模型,基于此模型给出了一种非线性系统故障检测与定位的新方法,它采用模糊聚类算法提取故障系统的模糊规则,进而完成系统故障的检测与定位,该方法对噪声污染具有较强的抑制作用,对模型误差亦无较高的要求, 仿真结果表明所提方法对非线性系统的故障可以及时地完成检测与定位。     

Fuzzy model-based fault detection and diagnosis for a pilot heat exchanger

This article addresses the design and real-time implementation of a fuzzy model-based fault detection and diagnosis (FDD) system for a pilot co-current heat exchanger. The design method is based on a three-step procedure which involves the identification of data-driven fuzzy rule-based models, the design of a fuzzy residual generator and the evaluation of the residuals for fault diagnosis using statistical tests. The fuzzy FDD mechanism has been implemented and validated on the real co-current heat exchanger, and has been proven to be efficient in detecting and isolating process, sensor and actuator faults.     

Fuzzy-based Refinement of the Fault Diagnosis Task in Industrial Devices

This paper first describes a fuzzy classifier to be used for fault diagnosis. Then, the paper presents a refinement of the diagnosis task performed with this fuzzy classifier. For each fault, a number of 20 levels of fault strength have been considered. In previous work, more than one single category per fault has been used to improve the classifier performance, i.e. distributing the strength levels into small, medium and, respectively large strength subsets. However, this distribution scheme is too rigid. This paper introduces a flexible distribution scheme that takes into account the (di)similarities between different strength levels. The refinement proposed here offers better insight on the behavior of each fault and it increases separation between overlapping faults, which improves the final outcome of the diagnosis process.     

一种基于模糊神经网络的故障分类器及其在多传感器故障诊断中的应用􀀁

提出了一种应用模糊神经网络进行故障诊断新方法．采用模糊神经网络作为故障分类器,离线地自适应从学习样本数据中提取各个用以描述故障状态的模糊参考模型．在诊断时,此模糊神经网络在线地得到当前系统的模糊模型描述,并将与各个参考模型相匹配,从而得出正确的诊断结果．它适用范围广泛,如用于控制系统的过程对象以及传感器、执行器故障的检测与诊断．通过对燃汽轮机控制系统多传感器故障诊断的仿真证明了此法的有效性和优越性．     

基于模糊聚类算法的故障数据分析与类型识别

为精确分析测量系统故障数据和识别故障类型,提出一种基于模糊聚类算法的故障数据分析方法。该方法首先用小波变换有效地检测出系统故障的微弱非线性不规则信号,再用模糊聚类的方法对故障进行分类识别。由于该算法在目标函数中加入隶属度函数,同时定义明可夫斯基的距离测度,因此能够克服K-means算法不适用于进行非凸形状的聚类的缺点,从而使诊断的数据更加精确。     

TPS自动生成平台上的模糊故障字典法

目前数字系统的故障诊断和测试性设计技术已经比较成熟,而模拟系统的故障诊断,尽管起步较早,但进展缓慢。这主要由于参数值的连续变化、反馈和非线性等原因,使得模拟系统的故障诊断相对比较复杂。目前已经提出了多种诊断方法,故障字典法即为其中的一种,它通常用于直流电路硬故障的诊断。由于故障因素和容差因素互相交迭,具有一定的模糊性,这给故障字典的应用带来一定的困难。提出了一种基于模糊故障字典的模拟电路故障诊断方法,用模糊故障字典取代传统的故障字典,适用于模拟电路单故障的故障诊断及故障隔离,便于计算机辅助诊断。     

基于模糊聚类算法的故障数据分析与类型识别

为精确分析测量系统故障数据和识别故障类型．提出一种基于模糊聚类算法的故障数据分析方法。该方法首先用小波变换有效地检测出系统故障的微弱非线性不规则信号,再用模糊聚类的方法对故障进行分类识别。由于该算法在目标函数中加入隶属度函数,同时定义明可夫斯基的距离测度．因此能够克服K-means算法不适用于进行非凸形状的聚类的缺点．从而使诊断的数据更加精确。     

Fault isolation in analog circuits using a fuzzy inference system

This paper introduces a fuzzy inference system (FIS) for single analog fault diagnosis. The ability of fuzzy logic to encode structured knowledge in a numerical framework is exploited in isolating faults in analog circuits. A training set that simulates the behaviour of the circuit due to a set of anticipated single faults as well as the fault-free situation is first constructed. For each anticipated fault, this set relates the circuit measurements to the corresponding deviation in the faulty circuit element from its nominal. These measurements and the deviations in circuit elements are both fuzzified into appropriate linguistic fuzzy values. A fuzzy rule base for each fault that characterizes the circuit response by linking symptoms to causes is built. The outputs of the fuzzy rule bases are then defuzzified to recover crisp values for the deviations in circuit elements. A fault diagnosis procedure that utilizes the proposed FIS is also presented along with a brief analysis and comparison with a number of existing artificial intelligence-based techniques. A test example that demonstrates the potential of this procedure in fault isolation is illustrated.     

Fault detection and diagnosis in process data using one-class support vector machines

In this paper, a new approach for fault detection and diagnosis based on One-Class Support Vector Machines (1-class SVM) has been proposed. The approach is based on a non-linear distance metric measured in a feature space. Just as in principal components analysis (PCA) and dynamic principal components analysis (DPCA), appropriate distance metrics and thresholds have been developed for fault detection. Fault diagnosis is then carried out using the SVM-recursive feature elimination (SVM-RFE) feature selection method. The efficacy of this method is demonstrated by applying it on the benchmark Tennessee Eastman problem and on an industrial real-time Semiconductor etch process dataset. The algorithm has been compared with conventional techniques such as PCA and DPCA in terms of performance measures such as false alarm rates, detection latency and fault detection rates. It is shown that the proposed algorithm outperformed PCA and DPCA both in terms of detection and diagnosis of faults.