一种新的模拟电路故障模糊集确定算法

提出了一种在模拟电路故障诊断中故障类模糊集的确定算法.该算法基于测前仿真法,不但同时适用于线性电路和非线性电路,而且可以确定元件模糊组以外的故障类模糊集.算法利用电路仿真故障特征数据计算故障的类间差异矩阵,从而完成故障类模糊集的确定.计算示例验证了该算法的有效性.     

基于电压灵敏度的模拟电路节点选择策略

测试节点选择问题是当前模拟电路故障诊断领域的研究热点之一。以节点电压灵敏度定义为基础,对电路中的故障状态进行模糊划分,定义测试节点上的电路故障模糊集,提出故障诊断能力指标的概念,并用其描述节点对故障的区分能力。在此基础上给出一种新的测试节点优选方法,最终实现以最少的节点数目覆盖最多的电路故障的目的,应用实例验证了新算法的有效性。     

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．     

基于模糊C均值聚类和转子轴心轨迹特征的转子状态诊断

针对现有轴心轨迹特征用于转子故障程度判别识别精度低、效果差的问题,提出一种基于轴心轨迹象限信息熵的轴心轨迹特征提取新方法。该方法将轴心轨迹按象限划分为四个区域,分别计算四个区域的信息熵作为故障特征,然后使用模糊聚类进行故障模式识别和故障程度判别。通过分析网格划分程度对于聚类效果的影响,确定了象限信息熵获取过程中关键参数的确定方法,进而通过聚类中心初始化,改善了模糊C均值算法聚类效果不稳定的问题。通过在实验台进行不同故障不同程度的故障模拟实验,将提出的新指标与现有轴心轨迹特征进行对比,结果表明该方法在识别效果和数据可视化方面表现卓著,为后期进行实时状态监测和故障精密诊断提供了新的思路。     

集成电路多故障测试生成算法研究

对组合电路的测试生成算法进行研究,介绍具有约束条件的布尔差分算法,还对时序电路的测试生成算法进行研究,九值算法比D算法在做D驱赶时要减少很多次无用的计算,在对电路进行描述时充分考虑了故障对电路的重复影响作用,可以对D算法无法产生测试的故障产生测试矢量.     

面向产品族的客户需求最佳聚类确定方法

以大规模定制为前提,为实现客户需求指导产品族规划的目的,建立了客户需求广义聚类模型．根据客户需求信息是否存在层次等级关系将其划分为递阶型和等价型,针对目前对递阶型客户需求聚类研究不能充分利用需求信息的问题,提出了基于模糊集的混合型客户需求聚类算法,增强了处理需求信息的能力．为实现产品族的合理规划,综合客户满意度和企业成本理想度建立可行性指标,提出了基于可行性确定客户需求最佳聚类的方法,并以最佳聚类方案指导产品族规划,在满足客户需求的同时保证了企业宽松的资金链．最后结合实例说明了该方法的实用性．     

集成多策略改进FCM算法的旋转机械故障数据聚类分析研究

针对旋转机械故障数据聚类分析中的初始聚类中心不确定和孤立点敏感问题,提出了一种集成多策略改进的模糊C均值(FCM)聚类方法。首先以故障数据集的决策属性为等价关系对数据集进行划分,得到若干个由等价关系导出的等价类;然后以每个等价类为可行域,采用均值漂移方法搜索故障数据类中心;最后以搜索到的类中心为FCM算法的初始聚类中心,通过核技术计算故障数据样本与相应类中心在高维特征空间中的欧氏距离,从而实现数据样本相似性的有效度量,并完成故障数据的模糊聚类。通过标准数据集和旋转机械故障数据集对方法的性能进行了验证及比较分析。结果显示,改进FCM算法的聚类性能相比传统FCM算法的聚类性能得到了明显提升,在收敛速度和聚类准确性两个性能指标上,改进的FCM算法比FCM算法具有显著优势。     

规则库自提取和简化的模糊系统

介绍了一种新的规则自提取和简化的模糊模型算法，该算法由3个步骤组成：首先，通过构造一个模糊软划分判别准则对输入输出空间进行模糊划分，得到最佳分类数，从而得到了最佳的规则数；其次，根据最佳规则数和神经网络来构造初始的模糊模型；第三，通过运用模糊相似分析法，可以得到计算2个模糊集相似度的方程，从而根据事先确定的阈值来合并相似的模糊集。这样就得到了一个既满足对精度的要求又简单且具有可解释性的模糊系统。最后，用该算法对我国全要素生产力进行了模糊规则的提取。     

基于系统搜索的容差模拟电路多参数故障诊断

针对容差条件下非线性模拟电路的多参数故障诊断问题,提出一种系统搜索算法求解故障参数,实现故障元件的定位和参数辨识。首先将非线性测试方程的多解求解转化为差分方程的初值问题;然后采用牛顿法迭代生成简单曲线簇,沿曲面方向进行测试方程的解搜索,并通过四阶龙格库塔法计算初值问题的数值解,显著提高方程组解的求解效率,降低漏解的风险;最后将故障集验证转化为容差约束下的线性规划问题求解,实现故障元件定位,并通过实际电路实验验证该算法的有效性。该算法适用于含BJT和CMOS等非线性电路的故障诊断,具有较高的诊断准确度和参数辨识准确度。     

一种改进的盲解卷积算法在轴承声学诊断中的应用

针对时域盲解卷积算法滤波器长度估计困难的缺点,提出一种基于遗传算法优化的改进算法。该算法利用遗传算法搜索最佳时延,解决了盲解卷积结果不确定问题,并改进了信号分量的聚类指标,采用峭度作为独立分量间距离测度,提高了信号分量聚类的准确性,获得了可靠的估计信号。计算机仿真和实际环境中故障轴承声信号提取实验验证了该算法的有效性。     

基于多故障假设的诊断策略研究与应用

 针对工程中最优单故障序贯测试难以隔离的多故障问题,利用Grunberg等人提出的多故障模糊组的概念,以最优单故障测试策略为基础,构造了一种多故障假设下的序贯测试算法．该算法以多故障状态集的最小碰集作为生成最优单故障策略的故障状态,生成最优单故障策略;以诊断决策树中各叶节点的多故障状态集为评判标准,确定系统的状态,完成多故障假设下的系统故障诊断;与连续使用单故障策略的方法相比,该方法提高了测试效率．以某型机载电子设备为例给出算例分析,验证了该算法隔离多故障的有效性．     

数字电路可测性设计的一种故障定位方法

在逻辑函数ReedMuller模式的电路可测性设计方面，文章采用AND门阵列和XOR门树结构来设计电路，提出了一种设计方案，可实现任意逻辑函数的功能，而且所得电路具有通用测试集和完全可故障定位的特点。给出了进行故障定位的方法，并可把它应用于其他相关电路的可测性设计。     

基于最小二乘法的GPS多天线测姿及精度分析

阐述GPS测姿基本原理和采用多天线测姿方法,针对影响GPS测姿精度因素进行分析,提出应用最小二乘法进行载体姿态参数计算和动态模糊度解算的方法.比对试验和精度分析表明,使用最小二乘法进行载体姿态确定是最优的,使用最小二乘搜索法进行动态模糊度解算是非常有效的.     

Sparse convolutional autoencoder-based fault location for drive circuits in nuclear reactors

Drive circuit is a critical part of instrumentation and control systems in nuclear reactors, and its performance directly influences the operation of nuclear reactors. However, comparing with the open circuit IGBT faults, soft faults caused by the degradation of electronic components present much slighter fluctuations to the performance of drive circuits. If the two fault modes co-exist, traditional fault diagnosis models are prone to misclassify soft faults as the normal condition. To improve the accuracy of fault diagnosis of drive circuits, it necessitates to accurately locate the faults of drive circuits, while effectively extracting the distinguishable fault features is one of the critical factors for fault location. In this article, a fault location method combining the empirical modal decomposition (EMD) algorithm and sparse convolutional autoencoder (SCAE) is proposed. The EMD algorithm is applied to decompose the three-phase current signals of drive circuits. An SCAE-based feature extractor is constructed to capture high-dimensional and sparse fault feature data with the aid of the powerful feature autonomic extraction capability of deep learning. A deep classifier is designed to locate faults in the driver circuit. A fault simulation model of the drive circuit is developed and the monitor data is collected. The effectiveness of the proposed method is validated via a real case of drive circuit in nuclear reactors.     

Soft fault detection and isolation in analog circuits: some resultsand a comparison between a fuzzy approach and radial basis functionnetworks

This paper provides a comparison between two techniques for soft fault diagnosis in analog electronic circuits. Both techniques are based on the simulation before test approach: a "fault dictionary" is a priori generated by collecting, signatures of different fault conditions. Classifiers, trained by the examples contained in the fault dictionary, are then configured to classify the measured circuit responses. The suggested classifiers have similar structures. The first is based on a fuzzy system, obtained by processing fault dictionary data for automatic generation of IF-THEN rules, and the second classifier is based on a radial basis function neural network. The two classifiers are used to detect and isolate faults both at the subsystem and component levels. The experimental results point out that both classifiers provide low classification errors in the presence of noise and nonfaulty components tolerance effects. The fuzzy approach provides better results due to an efficient generation method for the IF-THEN rules that allows adding IF parts in the input space regions where ambiguity occurs     

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.     

Implementation of the near-field signal redundancy phase-aberration correction algorithm on two-dimensional arrays

Near-field signal-redundancy (NFSR) algorithms for phase-aberration correction have been proposed and experimentally tested for linear and phased one-dimensional arrays. In this paper the performance of an all-row-plus-two-column, two-dimensional algorithm has been analyzed and tested with simulated data sets. This algorithm applies the NFSR algorithm for one-dimensional arrays to all the rows as well as the first and last columns of the array. The results from the two column measurements are used to derive a linear term for each row measurement result. These linear terms then are incorporated into the row results to obtain a two-dimensional phase aberration profile. The ambiguity phase aberration profile, which is the difference between the true and the derived phase aberration profiles, of this algorithm is not linear. Two methods, a trial-and-error method and a diagonal-measurement method, are proposed to linearize the ambiguity profile. The performance of these algorithms is analyzed and tested with simulated data sets.     

On the implementation of an analog ATPG: the nonlinear case

In order to reduce the complexity of the fault diagnosis equations and still retain computational simplicity, a self-testing algorithm has been proposed and implemented on a VMS VAX 11/780 for linear circuits. A prototype implementation of such an algorithm for nonlinear circuits and systems is presented. The proposed analog automatic test program generator (AATPG) for nonlinear circuits and systems is divided into offline and online processes. Unlike the simulation of the pseudocircuits in the linear case, which can be achieved by a matrix/vector multiplication, the circuit simulator SPICE is used to simulate the nonlinear pseudocircuits. The automatic SPICE code generator required for this simulation is presented. The proposed AATPG for nonlinear circuits has been implemented on a VMS VAX 11/780. The actual test can be run in either a fully automatic mode or interactively     

基于L-M算法的雷达板级电路快速故障诊断

新型雷达可更换的板级电路很多,为了快速诊断板级电路故障,将一种基于L-M算法的神经网络用于故障诊断;分析了L-M算法BP网络进行雷达板级电路故障诊断的原理;建立了故障诊断样本训练和测试平台;并以实例在该平台上对L-M算法BP网络进行训练和实际诊断,结果表明,该方法诊断准确性高,比其它方法更为快速有效,较好地解决了雷达可更换板级电路的故障诊断问题。