基于神经网络的变压器故障诊断方法研究

针对概率神经网络(PNN)模型强大的非线性分类能力,PNN能够很好地对变压器故障进行分类;文章通过对PNN神经网络的结构和原理的分析,应用PNN概率神经网络方法对变压器故障进行诊断;通过实例仿真表明,PNN网络的训练时间比BP网络少,比之预测准确度也要高,而且还具有高度的泛化能力,这使得PNN网络可以有效地运用到变压器故障诊断中,具有一定的可操作性。     

基于果蝇算法优化的概率神经网络在变压器故障诊断中的应用

传统的变压器故障诊断方法存在编码不全,容易错判漏判的缺点。随着变压器在线监测技术的发展和产品需求的增加,变压器故障诊断技术朝着智能化的方向发展。为提高故障诊断率,结合油中气体分析法,本文提出了一种基于果蝇算法优化的概率神经网络模型的变压器故障诊断方法。作为一种新型的启发式和进化式算法,果蝇优化算法具有易理解和快速收敛到全局最优解的优点。概率神经网络结构简单、训练简洁,具有强大的非线性分类能力,将样本空间映射到故障模式空间中,从而形成一有较强容错能力和机构自适应能力的诊断网络。采用果蝇算法对模型参数进行优化,减少人为因素对神经网络设计的影响。仿真实验证明这种基于果蝇优化算法的概率神经网络可以有效地运用到变压器故障诊断中,为变压器故障诊断供了一条新途径,具有良好的研究价值和发展前景。     

基于改进PSO优化PNN网络的变压器故障诊断方法

根据变压器产生故障时特征气体和故障类型的非线性关系,结合油中溶解气体分析方法,采用了基于改进粒子群-概率神经网络(PNN)的故障诊断方法.针对PNN网络平滑因子按照经验选取的不足,以及使用粒子群优化(PSO)该参数时搜索精度低、容易早熟收敛等缺点,改进粒子群引入遗传算法的变异操作,并在迭代中对惯性权重动态调整和加速因子的线性变化,并用于训练PNN神经网络平滑因子集合;然后将改进PSO-PNN神经网络应用于变压器故障诊断中,通过诊断测试验证了该方法的有效性.     

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。     

LF-PSO优化神经网络的高压断路器机械故障诊断

目前对高压断路器的故障诊断方法较多,其中采用神经网络方法居多。提出一种基于莱维飞行粒子群算法（LF-PSO）优化PNN神经网络的故障诊断技术。PNN结构简单,收敛速度快,但其中平滑因子σ对网络输出结果正确性影响较大,采用改进的粒子群算法对σ进行寻优。在标准粒子群基础上加入LF 能有效地使粒子通过随机游走产生新的解,经历新的搜索路径和领域,从而增加了种群的多样性,提高发现更优解的概率,不易陷入局部极值,提高了搜索的速度。通过实验数据验证,LF-PSO优化的PNN算法加快了搜索的速度,提高了诊断的精度,减小了误差,分类效果明显,是一种有效的故障诊断方法。     

Fault diagnosis based on pulse coupled neural network and probability neural network

In operation of mechanical equipment, fault diagnosis plays an important role. In this paper, a novel fault diagnosis method based on pulse coupled neural network (PCNN) and probability neural network (PNN) is presented. The shape information of shaft orbit provides an important basis for fault diagnosis. However, the feature extraction and classification of shaft orbit is difficult to realize automation. The PCNN technique has excellent performance in the feature extraction. In the present study, a PCNN combined with roundness method is used to extract the feature vector of shaft orbit, because time signature from a PCNN has the property of insensitive to rotation, scaling and translation. Meanwhile, roundness is also with the same properties. Further, the PNN is used to train the feature vectors and classify the vibration fault. By comparison with the back-propagation (BP) network and radial-basic function (RBF) network, the experimental result indicated the proposed approach achieved fast and efficient fault diagnosis.     

基于深度森林的无线传感器网络故障分类算法

针对无线传感器网络(WSN)节点容易出现故障从而导致网络瘫痪的问题,提出了一种基于改进的深度森林的无线传感器网络故障分类方法;深度森林是基于森林的集成学习方法,其输入是多维特征向量,特征向量将由多粒度扫描和级联森林这两个主要组成部分进行处理,多粒度扫描通过处理数据之间的关系来增强数据表示的能力,级联森林用于分类或预测;针对级联森林部分随着层数的增加可能造成的维数问题进行优化后,将该算法用于故障分类可以提高故障诊断的精确度;在仿真验证阶段,将该算法与深度神经网络(DNN)和支持向量机(SVM)算法进行对比;结果显示,该算法可以准确地识别出不同的故障类型,并且在损坏故障和电源故障的识别达到了最高精度,综合平均精度在98.4%;对偏移故障、漂移故障和通信故障的识别略低于卷积神经网络(CNN)算法,但综合训练时间、参数调节来看,该算法更能满足实际工程的需要。     

Fault diagnosis of power transformer based on support vector machine with genetic algorithm

Diagnosis of potential faults concealed inside power transformers is the key of ensuring stable electrical power supply to consumers. Support vector machine (SVM) is a new machine learning method based on the statistical learning theory, which is a powerful tool for solving the problem with small sampling, nonlinearity and high dimension. The selection of SVM parameters has an important influence on the classification accuracy of SVM. However, it is very difficult to select appropriate SVM parameters. In this study, support vector machine with genetic algorithm (SVMG) is applied to fault diagnosis of a power transformer, in which genetic algorithm (GA) is used to select appropriate free parameters of SVM. The experimental data from several electric power companies in China are used to illustrate the performance of the proposed SVMG model. The experimental results indicate that the SVMG method can achieve higher diagnostic accuracy than IEC three ratios, normal SVM classifier and artificial neural network.     

基于遗传算法小波神经网络的电力变压器故障诊断

本文提出了一种基于遗传算法小波神经网络的变压器故障诊断方法。首先构造了基于Mexicohat小波的小波神经网络,其次利用遗传算法优化小波网络的参数,并将其应用到基于溶解气体分析的变压器故障诊断中,最后通过实例证明了本方法的有效性和可行性。     

应用概率神经网络诊断自行火炮发动机的故障

研究了概率神经网络模型,并应用于故障诊断.对基于概率统计思想和Bayes分类规则的概率神经网络模型、网络结构、算法及其特点进行了分析,并提出一种优化估计平滑因子的方法.概率神经网络可很好地诊断自行火炮发动机运行中油路和气路的故障,在模式识别和故障诊断领域中可取得良好的应用效果.     

可拓神经网络在变压器故障诊断中的应用

可拓神经网络是一类新的神经网络,它结合了可拓学理论和人工神经网络技术。可拓神经网络已经在模式识别、故障诊断、分类聚类等领域有了成功的应用。针对变压器故障诊断的特点,提出一种基于可拓神经网络的电力变压器故障诊断方法。介绍了可拓神经网络;构造了基于可拓神经网络的故障诊断模型和算法设计,并将其应用到电力变压器的诊断识别;通过仿真实验验证了该方法简单易行、训练误差小、收敛时间快等优点。该方法具有一定的应用及推广能力。     

基于径向基神经网络的变压器故障诊断

研究了一种基于RBF神经网络的电力变压器故障诊断方法。该方法采用目前应用较多的隐含层为径向基函数的最小正交二乘法训练人工神经网络,克服了BP算法易陷入局部极小、收敛速度慢的缺点。利用MATLAB仿真实现,结果表明该方法具有速度快、诊断精度高等优点,能有效地运用于电力变压器故障诊断中。     

A self-adaptive data analysis for fault diagnosis of an automotive air-conditioner blower

This paper presents a fault diagnosis system for an automotive air-conditioner blower based on a noise emission signal using a self-adaptive data analysis technique. The proposed diagnosis system consists of feature extraction using the empirical mode decomposition (EMD) method and fault classification using the artificial neural network technique. The EMD method has been developed quite recently to adaptively decompose the non-stationary and non-linear signals. It sifts the complex signal of time series without losing its original properties and then obtains some useful intrinsic mode function (IMF) components. Calculating the energy of each component can reduce the computation dimensions and enhance classification performance. These energy features of various fault conditions are used as inputs to train the artificial neural network. In the fault classification, the probabilistic neural network (PNN) is used to verify the performance of the proposed system and compare with the traditional technique, back-propagation neural network (BPNN). The experimental results indicated the proposed technique performed well for quickly and accurately estimating fault conditions.     

基于支持向量机的机械故障智能分类研究

故障样本不足是制约故障诊断技术向智能化方向发展的主要原因之一，支持向量机（SVM)是一种基于统计学习理论(SLT)的机器学习算法，它能在训练样本很少的情况下达到很好的分类效果，从而为故障诊断技术向智能化发展提供了新的途径．本文介绍了支持向量机分类算法，以滚动轴承的故障分类为例，探讨了该算法在故障诊断领域中的应用，并与BP神经网络分类方法进行了对比研究，结果表明，SVM方法在少样本情况下的分类效果优于BP神经网络分类方法．     

基于LMS-PNN神经网络算法在心音识别与预测中的应用

传统的PNN神经网络具有很强的容错性、学习过程简单、训练速度快等特点,本文在传统PNN神经网络的基础上,利用LMS对其在心音分类方面进行优化,进而提高心音分类与预测的准确性。LMS-PNN神经网络算法对心音的信号运用窗函数进行分帧,利用双门限法确定数据的值,运用LMS算法对相应的参数进行调试,并将去噪后的数据以mat格式保存,提取出各个心音的短时自相关系数以及短时功率谱密度,并运用PNN神经网络,抽取40000个样本数据进行训练,并将各个心音进行等级划分与预测。 从PNN神经网络的模式层输入训练数据后,通过仿真测试可得,LMS—PNN神经网络预测准确率可达可达96%以上。     

An expert system for fault diagnosis in internal combustion engines using probability neural network

An expert system for fault diagnosis in internal combustion engines using adaptive order tracking technique and artificial neural networks is presented in this paper. The proposed system can be divided into two parts. In the first stage, the engine sound emission signals are recorded and treated as the tracking of frequency-varying bandpass signals. Ordered amplitudes can be calculated with a high-resolution adaptive filter algorithm. The vital features of signals with various fault conditions are obtained and displayed clearly by order figures. Then the sound energy diagram is utilized to normalize the features and reduce computation quantity. In the second stage, the artificial neural network is used to train the signal features and engine fault conditions. In order to verify the effect of the proposed probability neural network (PNN) in fault diagnosis, two conventional neural networks that included the back-propagation (BP) network and radial-basic function (RBF) network are compared with the proposed PNN network. The experimental results indicated that the proposed PNN network achieved the best performance in the present fault diagnosis system.     

Intelligent diagnosis method for a centrifugal pump using features of vibration signals

In the field of machinery diagnosis, the utilization of vibration signals is effective in the detection of fault, because the signals carry dynamic information about the machine state. However, knowledge of a distinguishing fault is ambiguous because definite relationships between symptoms and fault types cannot be easily identified. This paper presents an intelligent diagnosis method for a centrifugal pump system using features of vibration signals at an early stage. The diagnosis algorithm is derived using wavelet transform, rough sets and a partially linearized neural network (PNN). ReverseBior wavelet function is used to extract fault features from measured vibration signals and to capture hidden fault information across optimum frequency regions. As the input parameters for the neural network, the non-dimensional symptom parameters that can reflect the characteristics of a signal are defined in the amplitude domain. The diagnosis knowledge for the training of the PNN can be acquired by using the rough sets. We also propose a diagnosis method based on the PNN, one which can deal with the ambiguity problem of condition diagnosis, and distinguish fault types on the basis of the possibility distributions of symptom parameters automatically. The decision method of optimum frequency region for extracting feature signals is also discussed using real plant data. Practical examples of diagnosis for a centrifugal pump system are shown in order to verify the efficiency of the method.     

基于IPSO-BP神经网络的变压器故障诊断方法

标准粒子群优化(PSO)算法对惯性权重采取简单的线性衰减方案, 无法获得全局最优点. 为了弥补该方法的缺陷, 提出了一种改进的粒子群优化(IPSO)算法, 并将该算法与误差反向传播神经网络(BPNN)相结合, 进而提出一种基于IPSO-BPNN的变压器故障诊断新方法. 该方法将单个粒子连续被选为最优解的次数作为自适应变量, 并根据粒子的性能分类结果, 自适应地调整各粒子的惯性权重, 从而达到平衡局部和全局搜索能力的目的. 大量仿真表明该算法性能明显优于基于BPNN和PSO-BPNN的变压器故障诊断系统,     

基于LMS-PNN算法在心音识别与预测中的应用

传统的概率神经网络（Probability neural network, PNN）具有很强的容错性、学习过程简单、训练速度快等特点。为提高传统PNN在心音分类方面的性能,利用最小均方(Least mean square, LMS)方法对其进行优化,进而提高心音分类与预测的准确性。LMS-PNN算法对心音的信号运用窗函数进行分帧,利用双门限法确定数据的值,运用LMS方法对相应的参数进行调试,并将去噪后的数据以mat格式保存,提取出各个心音的短时自相关系数以及短时功率谱密度,并运用PNN,抽取40 000个样本数据进行训练,并对各心音进行等级划分与预测。从PNN的模式层输入训练数据后,由实验数据验证可知,LMS-PNN算法的预测准确率可达96%以上。