基于简化GFRF模型辨识算法的非线性模拟电路故障诊断

提出了基于简化GFRF(广义频率响应函数)模型辨识非线性电路的故障诊断方法.利用非线性频谱分析法获得被诊断系统的GFRF模型估计并提取能表征系统工作状况的电路特征,通过预设故障模式确定被诊断系统所处的工作状态从而定位系统故障.实验结果表明:基于非线性GFRF分析的故障诊断技术,无需在线辨识GFRF、计算量小,故障诊断效果较好.     

基于RBF-Volterra的非线性电路故障诊断

故障诊断的重要前提是故障特征可比性原则,而可比性原则的条件是模型结构的不变性,这就要求同一参数模型起码能稳定地拟合同一电路的所有故障状态。针对故障状态下非线性强弱变化造成的参数估计数值稳定性问题,提出一种基于RBF-Volterra级数的非线性电路故障诊断方法。在平移联动约束下,由基函数多项式(BFP)导出RBF-Volterra级数,分析了RBF-Volterra级数的截尾不截维特性和对非线性强弱变化的适应能力.RBF-Volterra级数不仅数值稳定性好,而且结构紧凑能有效避免维数灾难问题。最后,通过实例仿真说明该方法的有效性。     

基于Volterra核的MIMO非线性电路建模及智能特征提取

为了解决模拟乘法器等多输入测量电路的智能故障诊断准确率低的问题,文中研究了多输入多输出(MIMO)电路的基于Volterra级数的建模方法,为电路的故障诊断提供模型,提出了整体退火遗传特征提取方法,利用整体退火遗传算法的全局寻优能力优化故障诊断特征参数的提取,以选出各种故障状态之间特征差异最大的特征,以提高故障诊断的准...     

基于解轨迹拟合的非线性电路故障特征提取

本文针对一类斜波激励响应轨迹（解轨迹）存在且唯一的非线性电路,提出一种基于解轨迹拟合的故障特征提取方法。通过多项式拟合估计解轨迹麦克劳林多项式系数,由多项式系数间的迭代关系将非线性电路分解为线性子电路。在电路混合节点方程和解轨迹多项式系数基础上,提取各子电路节点故障电流为故障特征,结合多轨迹多项式系数矩阵奇异值分解实现非线性电路多故障特征提取。最后通过仿真实例说明该方法的有效性。     

基于小波分解的径流预报非线性模型

径流时间序列中包含了大量的信息,从频率域上来看,这些信息包括变化平稳的低频信息和变化相对剧烈的高频信息。本文利用小波分解,将径流时间序列分解为低频项和高频项,低频项采用逐步回归法预报,高频项采用基于自组织法求解的Volterra滤波器预报,两者结果综合,最终实现径流预报。实例计算表明,该模型具有足够高的计算精度。     

基于变分模态分解和谱峭度法的滚动轴承故障特征提取

针对共振解调中带通滤波器参数的选取通常比较困难,以及滚动轴承早期微弱故障信号通常被强烈的背景噪声淹没,为此,提出了使变分模态分解(variational mode decomposition,VMD)和谱峭度法共同作用来处理故障信号的方法。首先要重构故障信号,利用VMD分解得到故障信号的本征模态分量(intrinsic mode function,IMF),再计算各分量对应的峭度值对其自适应重构。然后,对重构信号进行快速谱峭度分析,并据此设计带通滤波器。最后,根据重构信号共振解调后的谱线即可准确判断轴承故障。通过处理实测数据进行诊断,结果表明了该方法较传统共振解调法诊断结果更精确。由此可见,谱峭度法在滤波器参数选择上具有可靠性,以及VMD与谱峭度结合能够降低噪声干扰提取微弱故障信号。     

基于小波包能量谱和ICA的模拟电路故障特征提取方法

针对模拟电路故障特征提取困难问题,提出一种基于小波包能量谱与独立成分分析相结合的模拟电路故障特征提取方法。首先通过仿真获取电路的故障输出信号,采用小波包分析对输出信号进行分解与重构,通过重构系数求取各频带的能量作为故障特征值。再利用独立成分分析算法对故障特征值进行优化,以此构造反映电路故障的特征向量。最后构造支持向量机,输入故障特征向量进行训练和测试,得出电路故障诊断准确率。仿真结果表明,该方法可以有效提取能够表征电路故障的特征参数,诊断准确率可达95.7%。     

基于FRFT-KPCA的模拟电路非线性故障特征提取

针对模拟电路受非线性及元件容差影响而导致响应信号在时域和频域都出现耦合,造成故障特征提取困难的问题,结合分数阶傅里叶变换和核主成分分析理论提出一种非线性故障特征提取方法。利用分数阶傅里叶变换对耦合信号进行预处理,采用粒子群优化算法寻找最优分数阶p,实现耦合信号在分数阶域最大程度的分离。采用核主成分分析对非线性特征进行维数压缩,实现故障特征提取。实验结果表明,在时域或频域相互耦合的信号经分数阶傅里叶变换后,在分数阶域上耦合程度明显减弱,核主成分分析能够有效处理信号中的非线性信息,特征提取效果要优于其他线性特征提取方法。经过分数阶傅里叶变换和核主成分分析相结合的方法所提取的故障特征使故障模式具有更好的可分性。     

基于退火遗传混合算法的模拟电路诊断激励优化

本文提出基于Volterra核和模拟退火遗传混合算法的模拟电路故障诊断激励优化方法.在以Volterra核为特征向量的模拟电路故障诊断中,以相同激励信号下电路各故障状态的特征向量的集总欧氏距离作为适应度函数,对用于激励的多频正弦信号的参数进行优化,首先利用模拟退火算法形成精英团队,然后利用遗传算法寻找最佳激励信号的参数,从而提高故障诊断的效率.文中给出了退火遗传混合算法的优化方案和流程,并通过实例加以验证.     

A novel method for fault diagnosis of hydro generator based on NOFRFs

Fault diagnosis and recognition of hydro generator are important issues which encounter in repair and security posture assessment. It influences the operational planning and security directly. Volterra series as an effective modeling method has been widely used in modeling and fault diagnosis of hydro generator, but the larger number of kernels limits its application in faults diagnosis. Non-linear output frequency response functions (NOFRFs) as a transformation style of Volterra series have a more intuitive visual and simple structure. In this paper, NOFRFs have been proposed to be employed in fault diagnosis of hydro generator, and a novel online identification method is proposed at the same time. Firstly, NOFRFs models of hydro generators are built. Secondly, a new method for online identification is proposed according to the operational characteristics of hydro generators. Finally, simulation verification has been done to demonstrate the advantage of the proposed method, and experimental studies are put forward on a hydro generator to analyze the failure mechanism in different states. The results indicate that the proposed method is useful and concise for fault diagnosis and nonlinear analysis of hydro generators.     

An algorithm for multiple fault diagnosis in analogue circuits

The paper deals with multiple fault diagnosis of analogue AC or DC circuits with limited accessible terminals for excitation and measurement and brings an algorithm for identificating faulty elements and evaluating their parameters. The main achievement is a method enabling us to efficiently identify faulty elements. For this purpose some testing equations are derived playing a key role in identification of possibly faulty elements which are next verified using a test of acceptance. The proposed approach is described in detail for double fault diagnosis. Also extension to triple fault diagnosis is given. Although the method pertains to linear circuits, some aspects of multiple fault diagnosis of non‐linear circuits can be also performed using the small signal approach. Two numerical examples illustrate the proposed method and show its efficiency. Copyright © 2006 John Wiley & Sons, Ltd.     

基于时间序列相似性挖掘的水电机组振动故障诊断研究

时间序列的相似性挖掘是水电机组故障诊断的重要方法,本文提出一种基于频率模糊贴近度的时间序列相似性的数据挖掘方法,用来解决水电机组故障诊断中振摆特征曲线的相似性比较问题。该方法将复杂的时域问题转化为频域问题,通过模糊贴近来度量时间序列之间的距离,刻画出数据时间序列的相似程度。该算法应用到大峡水电站二号机组的故障诊断中,结果表明,该方法能够对故障做出准确判别,分离各种故障类型。由于需要存储的数据比较少,速度快,非常适于水电机组故障诊断中大规模图形序列挖掘。     

Neural observer-based small fault detection and isolation for uncertain nonlinear systems

Small faults (some weak faults with a tiny magnitude) are difficult to detect and may cause severe problems leading to degrading the system performance. This paper proposes an approach to estimate, detect, and isolate small faults in uncertain nonlinear systems subjected to model uncertainties, disturbances, and measurement noise. A robust observer is developed to alleviate the lack of full state measurement. Using the estimated state, a dynamical radial basis function neural networks observer is designed in form of LMI problem to accurately learn the function of the inseparable mixture between modeling uncertainty and the small fault. By exploiting the knowledge obtained by the learning phase, a bank of observers is constructed for both normal and fault modes. A set of residues is achieved by filtering the differences between the outputs of the bank of observers and the monitored system output. Due to the noise dampening characteristics of the filters and according to the smallest residual principle, the small faults can be detected and isolated successfully. Finally, rigorous analysis is performed to characterize the detection and isolation capabilities of the proposed scheme. Simulation results are used to prove the efficacy and merits of the proposed approach.     

基于非线性控制理论的SSSC控制器的研究

提出一种运用非线性最优控制理论设计静止同步串联补偿器(SSSC)控制器的方法。在分析SSSC原理的基础上，导出SSSC的动态数学模型。并运用MATLAB的仿真工具建立了SSSC的仿真模型，对含SSSC的单机无穷大系统发生三相短路故障进行仿真。仿真结果表明，采用非线性控制器，SSSC能有效地改善电力系统的稳定性。     

基于遗传算法的数模混合电路故障诊断

本文针对数模混合电路故障诊断特点,采用Multisim仿真向电路注入故障模式,提出采用遗传算法进行故障诊断的方法。在对遗传算法的基本原理和步骤进行介绍的基础上,运用一种改进的遗传算法对具体电路进行了故障诊断,算法实例证明了该方法的有效性。     

基于小波神经网络的模拟电路故障诊断改进

在运用神经网络进行模拟电路故障诊断的过程中,代表着故障特征的网络输入至关重要,由于小波变换的时频局部化和多尺度分析等特性,将两者结合起来,通过小波变换对模拟电路的输出响应进行故障特征提取,同时解决PSPICE与MATLAB之间的数据通信问题,提出将蒙特卡罗分析产生的所有训练样本经过处理后输入到一个神经网络进行训练的方法,从而避免了训练多个神经网络。利用神经网络对各种故障模式进行分类,实现模拟电路的故障诊断,并进一步与传统的BP网络故障诊断法进行比较。仿真结果表明,该方法可以实现故障检测及定位,诊断的准确率显著提高,适用于模拟电路故障诊断。     

一种基于 DSP的电力电子电路在线故障诊断方法

以三相桥式整流电路为例,首先对可控整流装置正常工作和故障运行时整流电压波形进行分析和归类,定义了一种"面积"并建立了故障模型,提出了一种改进的谱分析故障诊断方法,根据其特征值进行诊断定位;其次归纳出实验算法并用 DSP系统实现,实验表明该方法实现了可控整流装置的在线故障诊断和监测;最后总结了该方法的特点并推广至其它形式整流电路.