基于微分进化神经网络的模拟电路故障诊断

BP神经网络已在模拟电路故障诊断领域得到广泛应用,但BP神经网络存在训练速度慢且容易陷入局部最优的问题.由此,本文提出了一种基于混合变异策略的微分进化改进算法,描述了利用微分进化改进算法进行神经网络权值训练的过程和方法,并将微分进化神经网络用于模拟电路故障诊断,文中还对微分进化神经网络与BP神经网络进行了比较.实验结果表明,微分进化神经网络的训练时间和训练精度均优于BP神经网络,其在模拟电路故障诊断中的准确度比BP神经网络提高了7%.     

应用概率神经网络诊断自行火炮发动机的故障

目的　研究概率神经网络模型 ,并应用于故障诊断 .方法　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、算法及其特点进行分析 ,利用其进行故障诊断 ,并提出一种优化估计平滑因子的方法 .结果　概率神经网络可很好地诊断自行火炮发动机进行中油路和气路的故障 .结论　概率神经网络在模式识别和故障诊断领域中可取得良好地应用效果     

应用概率神经网络诊断自行火炮发动机的故障

目的 研究概率神经网络模型,并应用于故障诊断。方法 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、算法及其特点进行分析,利用其进行故障诊断,并提出一种优化估计平滑因子的方法。结果 概率神经网络可很好地诊断自行火炮发动机进行中油路和气路的故障。结论 概率神经网络在模式识别和故障诊断领域中可取得良好的应用效果。     

复杂电力电子主回路故障诊断研究与仿真

根据双桥串联可控整流电路输出故障电压具有周期性、平移性、随着控制角大小变化波形伸展的特点,按晶闸管序号进行了故障分类,共有25大类、294小类,提出了实时采样的快速傅立叶变换＋概率神经网络的故障诊断方法．首先对整流输出电压实时采样值进行快速傅立叶变换得到幅值和相位,然后根据幅值信息利用概率神经网络进行故障大类识别,利用相位信息进行故障小类识别．仿真结果表明,该方法诊断结果正确、实时性好、硬件实现简单,对复杂电力电子主回路的故障诊断具有普遍适用性．     

分形特征的模拟电路故障诊断方法

针对模拟电路中存在的非线性问题,提出一种以模拟电路分形特征为输入量的故障诊断方法。通过对多测试分量数据进行分形特征提取,输入神经网络建立信息融合中心融合处理各分形特征量,利用多源性互补信息减少模拟电路故障诊断的不确定性。实验结果表明,该故障诊断方法可准确地检测出模拟电路中的故障现象。     

基于灵敏度预分类的BP神经网络故障诊断方法

故障特征信息的获取和处理对电路故障的可靠分类和准确诊断有很大的影响．在电路故障诊断时,对于不同的故障模式,存在信息混叠的现象,需要解决特征信息的有效提取和故障的可靠分类等问题．为此,本文提出了一种结合灵敏度特性分析的BP神经网络故障诊断方法．基本思想是通过灵敏度的计算,对电路故障样本作预分类,再根据电路灵敏度的计算结果分别提取相应特征信息,以此构造故障样本特征集,然后作为BP神经网络的输入对网络训练,并进行故障诊断．对滤波器的仿真结果表明,该方法能分类不同的元件故障,且对模拟电路故障诊断的平均正确率优于传统方法．     

模拟电路故障原因与诊断方法

随着电路系统集成度的不断增大,模拟电路中的故障成本占据集成电路总诊断成本的绝大部分,因此加强模拟电路故障诊断与排除的研究十分重要。首先分析模拟电话故障的类型与原因,随后详细介绍几种故障的诊断方法。     

论模拟电路的故障诊断

随着电路系统复杂性的提升,模拟电路故障率占据总故障率的大部分,所以,加强模拟电路故障诊断与排除的研究十分重要。从分析模拟电路的故障类型及原因出发,探讨在模拟电路故障诊断过程中所采用的几种常见诊断方法,以及未来的发展趋势。     

基于小波分析和模糊神经网络的齿轮故障诊断研究

建立齿轮故障信号采集模拟试验台,结合小波分析特征提取方法和模糊神经网络对齿轮故障进行了诊断,通过实验仿真,取得了很好的诊断结果。相比于传统的BP神经网络诊断方法,无论在诊断速度还是诊断精度上,模糊神经网络更具有优势。     

基于遗传神经网络信息融合的模拟电路故障诊断

提出了一种基于动态电源电流和输出电压信息融合,以及遗传算法优化径向基(GA-RBF)神经网络的电路故障诊断方法.对电路采样信号进行了小波包能最特征提取、归一化和特征关联,用遗传算法优化径向基神经网络的训练,建立了一个信息融合和故障诊断系统.通过仿真实验表明,此方法可以有效提高诊断正确率.     

Analog fault diagnosis of actual circuits using neural networks

We have developed a neural-network based analog fault diagnostic system for actual circuits. Our system uses a data acquisition board to excite a circuit with an impulse and sample its output to collect training data for the neural network. The collected data is preprocessed by wavelet decomposition, normalization, and principal component analysis (PCA) to generate optimal features for training the neural network. This ensures a simple architecture for the neural network and minimizes the size of the training set required for its proper training. Our studies indicate that features extracted from actual circuits lie closer to each other and exhibit more overlap across fault classes compared to SPICE simulations. This implies that the neural network architecture which can most reliably perform fault diagnosis of actual circuits is one whose outputs estimate the probabilities that input features belong to different fault classes. Our work also shows that SPICE simulations can be used to select appropriate features for training the neural network. Reliable diagnosis of faults in an actual circuit, however, requires training data from the circuit itself. Our fault diagnostic system, trained and tested using data obtained from real sample circuits, achieves 95% accuracy in classifying faulty components     

基于Volterra频域核的非线性模拟电路故障诊断

非线性模拟电路的故障特征的表征困难,另外基于神经网络的故障诊断存在收敛速度低及泛化能力差的问题.本文将非线性系统复杂的Volterra高阶核函数表示成一维数值解的形式,并以此来表征故障特征,同时借助小波变换与神经网络的优点,降低了输入特征矢量的维数,并且提高了泛化能力.实验结果表明:该方法建模简单,运算量小,具有一定的...     

Fault Diagnosis of Power Electronic Circuits Based on Adaptive Simulated Annealing Particle Swarm Optimization

In the field of energy conversion, the increasing attention on power electronic equipment is fault detection and diagnosis. A power electronic circuit is an essential part of a power electronic system. The state of its internal components affects the performance of the system. The stability and reliability of an energy system can be improved by studying the fault diagnosis of power electronic circuits. Therefore, an algorithm based on adaptive simulated annealing particle swarm optimization (ASAPSO) was used in the present study to optimize a backpropagation (BP) neural network employed for the online fault diagnosis of a power electronic circuit. We built a circuit simulation model in MATLAB to obtain its DC output voltage. Using Fourier analysis, we extracted fault features. These were normalized as training samples and input to an unoptimized BP neural network and BP neural networks optimized by particle swarm optimization (PSO) and the ASAPSO algorithm. The accuracy of fault diagnosis was compared for the three networks. The simulation results demonstrate that a BP neural network optimized with the ASAPSO algorithm has higher fault diagnosis accuracy, better reliability, and adaptability and can more effectively diagnose and locate faults in power electronic circuits.     

基于注意力BiGRU的机械故障诊断方法研究

为了解决机械故障诊断领域传统方法自适应性差、参数选择过于依赖人工的问题,提出了一种基于循环神经网络的机械故障诊断算法。该方法利用预处理后的机械振动信号,搭建了双向门控循环单元的故障诊断模型,并进行了基于注意力机制的模型优化,提高了特征提取效率。经过美国凯斯西储大学轴承数据集以及自采集的柴油机故障实验数据验证,相比于传统神经网络算法提升了计算效率和诊断准确率,并表现出了良好的抗噪能力。结果表明,该方法可以有效适用于基于机械振动信号的故障诊断,具有一定的工程应用价值。     

A Modular Fault-Diagnostic System for Analog Electronic Circuits Using Neural Networks With Wavelet Transform as a Preprocessor

We have developed a modular analog circuit fault- diagnostic system based on neural networks using wavelet decomposition, principal component analysis, and data normalization as preprocessors. Our proposed system has the ability to identify faulty components or modules in an analog circuit by analyzing its impulse response. In this approach, the circuit is divided into modules, which, in turn, are divided into smaller submodules successively. At each level, where a module is divided into submodules, a neural network is trained to identify the submodule that inherits the fault of interest from the parent module. This procedure finds the faulty component or module of any desirable size in an analog circuit by consecutive divisions of modules as many times as necessary. Our proposed approach has three advantages over the traditional neural-network-based diagnostic systems, which directly look for faulty components in the entire circuit. First, the performance of the modular systems is reliable and robust independent of the circuit size and can successfully classify similar fault classes with a significant overlap in the feature space where the traditional approach completely fails. Second, the modular approach requires significantly smaller neural network architectures, leading to much more efficient training. Third, for large real circuit boards, our diagnostic system proceeds to systematically reduce the size of the faulty modules until it is feasible to replace it.     

基于专家系统与神经网络集成的故障诊断的应用研究

本文针对工业生产中使用的直流电动机,应用人工智能的相关理论对其故障进行了广泛深入地研究。在此基础上,探讨了专家系统与人工神经网络相集成的电动机故障智能诊断方法并加以实现。实践证明,网络的学习时间显著缩短,整个系统的推理效率明显提高,并验证了集成式专家系统的诊断效果比传统的专家系统或神经网络更为全面、准确和迅速。电动机故障的集成式智能诊断方法是一个既有理论研究意义又有实际使用价值的课题与方向。     

Wavelet Transform and Neural Networks in Fault Diagnosis of a Motor Rotor

In the motor fault diagnosis technique,vibration and stator current frequency components of detection are two main means.This article will discuss the signal detection method based on vibration fault.Because the motor vibration signal is a non-stationary random signal,fault signals often contain a lot of time-varying,burst properties of ingredients.The traditional Fourier signal analysis can not effectively extract the motor fault characteristics,but are also likely to be rich in failure information but a weak signal as noise.Therefore,we introduce wavelet packet transforms to extract the fault characteristics of the signal information.Obtained was the result as the neural network input signal,using the L-M neural network optimization method for training,and then used the BP network for fault recognition.This paper uses Matlab software to simulate and confirmed the method of motor fault diagnosis validity and accuracy.     

神经网络专家系统在数控机床机械故障诊断中的应用

依据单一的专家系统或神经网络在处理故障诊断中各自存在着局限性,提出将神经网络技术与专家系统融合的集成式故障诊断专家系统,并用于数控机床的机械故障诊断中。介绍神经网络专家系统结构、特点及诊断方法,利用获得的机床机械故障知识、故障样本数据对系统进行验证。结果表明该系统人机界面友好,操作简单,有效地提高数控机床机械故障诊断的水平和效率。