基于小波分析的RAIM算法卫星故障检测研究

作为一种传统的卫星故障诊断方法,RAIM在卫星故障监测中发挥着重要作用;文中基于奇偶矢量RAIM算法,提出一种基于小波分析的RAIM算法的卫星故障检测方法;首先利用Haar小波分解,对卫星故障信号进行小波变换处理,提取故障信号特征,通过在不同尺度的奇异性变化找到信号突变点,然后将检测结果反馈给RAIM算法进行故障检测,对不同条件下的卫星故障进行故障检测性能进行分析;仿真实验表明,该算法提高了系统故障检测的准确性,降低了误警率。     

一种高精度电力谐波检测法

本文介绍了一种基于小波分析的谐波检测方法。小波多分辨分析可以对信号进行有效的时频分解，因而能将信号的不同频率成分分开。检测小波变换系数模的局部极值点可以检测到信号的突变点，即检测到信号频率跳变的时间，这是FFT谐波检测不能实现的。文中采用MATLAB小波分析工具箱对该方法进行了实验验证。     

小波变换在微机继电保护中的仿真研究

根据小波分析的基本理论 ,结合系统发生故障短路时故障相电流的波形特征 ,介绍了利用小波分解、重构后的高频分量突变点来检测是否发生故障。通过仿真分析表明 :该方法是有效的 ,并给出了基于文献 [1]算法的微机保护电路构成示意图。最后 ,分析了故障信号的消噪方法     

基于EMD的小波脊线法轨道检测

轨道的波浪弯曲不平顺是引起列车振动的直接因素。针对波浪不平顺提出了一种新的检测方法。基于经验模态分解的小波脊线法,滤去原始信号的高频部分,再对低频分量进行EMD处理,提取包含了故障信息的固有模态函数分量（IMF）的小波脊线。通过分析小波脊线的时频域,检测出突变信号发生的时刻。对列车在某线路的实测数据分析,研究结果表明,基于EMD的小波脊线法能方便而有效地检测出信号的突变成分,从而准确地识别轨道的不平顺位置。     

基于小波网络的传感器故障检测

针对常规BP网络存在的问题，在小波网络的基础之上将其结构进行改进提出复合小波网络，代替BP网络用于传感器的故障检测，为避免由于传感器输入信号突变引起的传感器输出与观测器预测值产生较大偏差而导致的误诊断，提出将检测方法加以改进。仿真实验表明，复合小波网络较BP网络和未改进的小波网络收敛速度快、预测精度高，更适用于故障检测；采用了复合小波网络的改进的检测疗法既提高了检测的快速性，义提高了可靠性。     

小波分析应用于直流系统接地故障检测

针对直流系统接地故障检测中常用的低频信号注入法容易受到各支路电缆中存在的对地电容的影响这一问题，提出了利用小波分析实现故障信号特征的提取的方法。分析了低频信号注入法的原理及存在的缺陷，针对其故障信号的特点，提出了基于小波变换的直流系统接地故障检测方案。基于小波去噪的原理，通过选择适当的小波函数，对支路电流信号进行分析和处理。经过仿真实验分析。表明该方法可以克服对地电容对接地故障检测的不利影响，完成故障信号分析，实现故障支路的准确定位。     

基于复合小波网络的传感器故障在线检测仿真研究

针对人工神经网络存在的问题,提出将小波分析与神经网络融合构成的小波网络用于传感器的故障诊断中.为进一步提高小波网络的性能,在小波网络的基础之上将其结构进行改进提出复合小波网络.为避免由于传感器输入信号突变所引发的传感器故障误诊断,将检测方法加以改进.仿真实验表明,复合小波网络具有更快的收敛速度和更好的预测性能,更适用于传感器的在线故障检测;同时,改进的检测方法既提高了检测的快速性,又提高了可靠性.     

基于小波分析的转子绕组匝间短路故障诊断方法

分析了汽轮发电机发生转子绕组匝间短路故障时的电磁特性,提出了基于小波分析的故障检测与诊断方法.这种方法是在探测线圈法的基础上,将小波变换用于突变信号的检测,对气隙中感应电动势信号的奇异特征进行提取,根据这些奇异特征,可实现对发电机转子绕组匝间短路故障的检测及故障点的定位.仿真分析表明,该方法适用于故障信号的奇异特征提取,适合于汽轮发电机转子绕组匝间短路故障的在线检测.     

储罐底板的漏磁检测信号处理中小波奇异性检测理论的应用

漏磁检测是无损检测中的一种重要方式,采用漏磁法采集到的漏磁信号具有数据量大和附带有大量的噪声的特性,其中,缺陷通常表现为输出信号发生突变,因此考察信号突变点的变化对漏磁检测有很重要的意义。本文利用了小波分析其独特的时域和频域特性,分析了基于小波变换的漏磁信号奇异点的定位方法和奇异性程度的计算方法,对漏磁信号进行处理,使信号便于存储和分析,仿真结果表明该方法是有效的。     

电子信息系统中利用小波分析进行故障诊断

在电子信息系统中经常要对系统的反馈信号进行分析,以找到系统内部的故障.然而,系统反馈的信号往往带有较大的噪声,会给故障信号的提取带来困难.而且,故障信号多是突变信号,传统的Fourier分析由于在时城不能局部化,难以检测到突变信号.小波分析优于傅里叶分析之处在于,小波分析在时域和频域同时具有良好的局部化性质,它可以对高频成分采用逐渐精细的时域或空间域取代步长,从而可以聚焦到对象的任意细节.这里主要讨论使用小波分析检测信号的奇异点,以及动态系统的频谱结构变化及参数突变,以及在实际的温度控制系统中利用小波分析进行故障检测,并利用MATLAB等工具进行仿真.对小波分析在故障检测中的优缺点进行了探讨.     

一种基于小波神经网络故障检测方法的仿真研究

文中提出了一种基于小波神经网络一性观测器的故障检测方法。它是一种把信号分析和模型相结合的故障检测方法,通过小波对信号的去噪和神经的神经网络的自学习功能,来获取系统输入输出的非线性动力学特性,进而实时计算出残差并进行逻辑判疡,可提高故障检测的速度和准确率。对同步交流电机的结构损伤故障进行了仿真,结果表明了该方法是可行的。     

一种基于智能移动代理的网络故障检测系统

随着网络规模的急剧扩大和结构的日趋复杂化,网络故障管理越来越重要.在一个复杂的通信网络中,故障是不可避免的,但是对故障的及时探测和识别对于提高网络的可靠性是非常重要的.监测数据包是网络故障检测中常用的方法,但是在大规模网络中会产生巨量的数据包.为此,人们提出了几种方法.但是这些方法都是建立在集中式管理体系结构之上的,因而没有良好的可扩展性、灵活性和本地处理能力.针对这些问题,提出了一种基于智能移动代理的网络故障检测系统结构,并用Java和aglet实现.实验表明,这种系统结构对于网络故障的检测是非常有效的     

Statistical signal processing approaches to fault detection

The parity space approach to fault detection and isolation (FDI) has been developed during the last 20 years, and the focus here is to describe its application to stochastic systems. A mixed model with both stochastic inputs and deterministic disturbances and faults is formulated over a sliding window. Algorithms for detecting and isolating faults on-line and analyzing the probability for correct and incorrect decisions off-line are provided. A major part of the paper is devoted to discussing properties of this model-based approach and generalizations to cases of incomplete model knowledge, and non-linear non-Gaussian models. For this purpose, a simulation example is used throughout the paper for numerical illustrations, and real-life applications for motivations. The final section discusses the reverse problem: fault detection approaches to statistical signal processing. It is motivated by three applications that a simple CUSUM detector in feedback loop with an adaptive filter can mitigate the inherent trade-off between estimation accuracy and tracking speed in linear filters.     

An active approach for detection of incipient faults

The methodology of auxiliary signal design for robust fault detection based on a multi-model (MM) formulation of normal and faulty systems is used to study the problem of incipient fault detection. The fault is modelled as a drift in a system parameter, and an auxiliary signal is to be designed to enhance the detection of variations in this parameter. It is shown that it is possible to consider the model of the system with a drifted parameter as a second model and use the MM framework for designing the auxiliary signal by considering the limiting case as the parameter variation goes to zero. The result can be applied very effectively to many early detection problems where small parameter variations should be detected. Two different approaches for computing the test signal are given and compared on several computational examples.     

LPV model-based robust diagnosis of flight actuator faults

A linear parameter-varying (LPV) model-based synthesis, tuning and assessment methodology is developed and applied for the design of a robust fault detection and diagnosis (FDD) system for several types of flight actuator faults such as jamming, runaway, oscillatory failure, or loss of efficiency. The robust fault detection is achieved by using a synthesis approach based on an accurate approximation of the nonlinear actuator–control surface dynamics via an LPV model and an optimal tuning of the free parameters of the FDD system using multi-objective optimization techniques. Real-time signal processing is employed for identification of different fault types. The assessment of the FDD system robustness has been performed using both standard Monte-Carlo methods as well as advanced worst-case search based optimization-driven robustness analysis. A supplementary industrial validation performed on the AIRBUS actuator test bench for the monitoring of jamming, confirmed the satisfactory performance of the FDD system in a true industrial setting.     

The process chemometrics approach to process monitoring and fault detection

Chemometrics, the application of mathematical and statistical methods to the analysis of chemical data, is finding ever widening applications in the chemical process environment. This article reviews the chemometrics approach to chemical process monitoring and fault detection. These approaches rely on the formation of a mathematical/statistical model that is based on historical process data. New process data can then be compared with models of normal operation in order to detect a change in the system. Typical modelling approaches rely on principal components analysis, partial least squares and a variety of other chemometric methods. Applications where the ordered nature of the data is taken into account explicitly are also beginning to see use. This article reviews the state-of-the-art of process chemometrics and current trends in research and applications.     

基于LPV模型的鲁棒故障检测滤波器设计

针对航空发动机动态过程中由于模型误差造成故障检测误报的问题,采用雅克比方法建立航空发动机动态线性变参数(LPV)数学模型反映发动机动态特性,利用特征结构配置法设计故障检测滤波器,较好地消除了模型偏差对故障检测准确率的影响.通过某型涡扇发动机控制系统传感器典型软、硬故障检测仿真实验表明,该方法提高了残差信号对模型误差和未知输入信号的鲁棒性,对发动机加减速过渡过程中故障检测准确度高,实时性好.     

An evolving approach to unsupervised and Real-Time fault detection in industrial processes

Fault detection in industrial processes is a field of application that has gaining considerable attention in the past few years, resulting in a large variety of techniques and methodologies designed to solve that problem. However, many of the approaches presented in literature require relevant amounts of prior knowledge about the process, such as mathematical models, data distribution and pre-defined parameters. In this paper, we propose the application of TEDA – Typicality and Eccentricity Data Analytics – , a fully autonomous algorithm, to the problem of fault detection in industrial processes. In order to perform fault detection, TEDA analyzes the density of each read data sample, which is calculated based on the distance between that sample and all the others read so far. TEDA is an online algorithm that learns autonomously and does not require any previous knowledge about the process nor any user-defined parameters. Moreover, it requires minimum computational effort, enabling its use for real-time applications. The efficiency of the proposed approach is demonstrated with two different real world industrial plant data streams that provide “normal” and “faulty” data. The results shown in this paper are very encouraging when compared with traditional fault detection approaches.     

基于稀疏主元分析的过程监控研究

主元分析（principal component analysis）是一种多元统计技术,在过程监控和故障诊断中具有广泛的应用。针对过程监控中数据量大的特点,提出一种稀疏主元分析（sparse principal component analysis）方法,通过引入lasso约束函数,构建稀疏主元分析的框架,将PCA降维问题转化为回归最优化问题,从而求解得到稀疏化的主元,并提高了主元模型的抗干扰能力。由于稀疏后主元相关的数据量减少,利用数据建立过程监控模型,减少了计算量,并缩短了计算时间,进而提高了监控的实时性。利用田纳西伊斯特曼过程（TE processes）进行实验仿真,并与传统的主元分析方法进行对比研究。结果表明,新提出的稀疏主元分析方法在计算效率和监控实时性上均优于传统的主元分析方法。     

智能控制下机械式自动变速器故障检测方法

考虑到当前机械式自动变速器故障检测方法由于故障种类划分能力较差,导致复合故障检测结果正确率较低的情况,设计智能控制下机械式自动变速器故障检测方法。设定信号采样频率,对采集后的信号进行离散处理,提取自动变速器振动信号。使用LSSVM模型构建支持向量机,完成振动信号训练处理。根据机械控制理论结合证据分类检测方法,完成自动变速器故障诊断。至此,智能控制下机械式自动变速器故障检测方法设计完成。构建实验环节,经实验结果证实,新型检测方法的复合故障检测结果正确率得到明显提升,在日后的研究中可应用此方法完成故障检测过程。