低压配电网单相接地故障选线方法的研究

根据低压配电网发生单相接地故障时的暂态特征，利用小波变换检测信号奇异性的功能，本文提出了用小波变换系数进行故障选线的原理。通过在MATLAB中仿真分析验证，该算法是现实可行的，结果是令人满意的。     

基于小波分析的电缆行波故障定位仿真研究

本文首先对电缆的故障波形进行了仿真分析,得到电缆故障点击穿和未击穿时的特征波形。利用小波分析法在信号消噪、奇异性检测等方面的优异性能,提出了基于小波奇异性检测原理的电缆故障定位技术。该技术利用小波分析所具有的时频局部特性,来分析电力电缆行波故障测距中反射脉冲的奇变点。在对此技术进行理论分析的基础上,利用计算机仿真证实这一技术的正确性、有效性。     

一种基于子波变换的语音增强方法

在分析了随机噪声的子波变换系数在不同尺度上的传递特性和噪声信号奇异性与子波模极大值的关系后,提出了用一尺度间变化的门限阈值来抑制带噪训音信号在不同尺度上噪声子波系数,从而实现了在重构信号中消除噪声的目的。文中还给出了不同信噪比语音信号的子波去噪的计算机仿真结果,从结果上看出,本文的方法有较好的语音去噪、增强效果。     

信号奇异性检测在铁路数据分析处理中的应用

根据目前铁路大力推广的ZPW2000-A自动闭塞方式的信号特点,利用基于小波分析技术的信号奇异性检测方法,通过机车信号记录器接收的波形信号进行奇异性检测,克服了传统傅立叶检测方法的局限性,满足铁路现场实际运用的需要。     

基于新阈值函数的电动机故障信号小波去噪

针对采集的电动机故障信号提取特征频率时产生的噪声干扰,提出了一种新的小波阈值函数,并将其与分层阈值相结合用于故障信号的小波去噪。新阈值函数很好地克服了软、硬阈值函数的缺点,分层阈值符合噪声分解系数随着分解尺度增大而迅速衰减的特点。对仿真信号及实测工程信号的分析结果表明,该算法有良好的去噪效果并有助于提高故障诊断的准确性。     

微损小波分解航空行星齿轮故障特征及诊断

提出基于dbN小波算子小波变换分解行星齿轮故障特征的故障检测算法,算法可以在轻微损坏时就较好地判断出齿轮故障。通过分析行星齿轮的故障成型,以及故障的冲击振动传播路径,建立其故障模型,在利用小波变换方法对其故障信号进行分析,检测方法可以较好地进行故障识别与检测,对比传统的傅里叶变换,新算法可以对微弱信号识别,能同时判断缺陷轮齿的频率和出现的时间,在排除故障时能准确分析故障位置,以及故障类型。仿真实验表明改进算法小波变换,具有弱信号检测能力强,准确判断故障出现时间,为尽早发现故障与解决故障问题提供了保障,故障检测概率提高了15%。具有较好的工程实用性。     

重载减速齿轮微局部缺陷能量谱故障特性分析

提出基于希尔伯特单边变换以及傅里叶变换分解重载减速齿轮故障特征调幅信号的能量谱故障特性分析算法,算法可以在重载减速齿轮出现局部损坏时就较好的判断出齿轮故障。通过分析重载减速齿轮的故障特性以及故障的冲击振动传播路径,建立其故障模型,在利用希尔伯特单边变换以及傅里叶变换对其故障信号进行分析能量谱提取,检测方法可以较好的进行故障识别与检测。仿真实验表明新算法可以对微弱信号识别,能根据齿轮故障的模型可以同时判断缺陷轮齿的频率和出现的时间,故能准确检测到局部细微齿轮缺陷,为尽早发现故障与解决故障问题提供了保障。     

小波分析在列车自动控制系统中的应用

在中国列车运行自动控制系统(CTCS)中传输存在的问题中,利用基于小波分析技术的信号奇异性检测方法对车载设备接收的信号进行奇异性检测,克服了传统傅立叶检测方法的局限性,具有简单、快速、准确和高效的特点,能够满足铁路现场开行200km/h动车组的需求。     

固态废弃物渗滤液的污染检测方法优化

在对固体废弃物渗滤液的污染检测过程中,由于传统方法是采用渗滤液迅速导电的特点进行污染检测的,渗滤液导电率一旦受到外界因素的影响,就会产生电流波动,造成检测误差大的问题。为此,提出了基于小波熵算法的固态废弃物渗滤液的污染检测优化方法。利用小波系数阈值对固态废弃物渗滤液的污染信号特征及软阀值进行有效的提取,将软阈值映射在具有污染信号能量分布特性的小波熵范围中,依据污染信号在不同分解尺度上会带有不同小波熵的特点,可以自适应地确定污染信号高频系数分量的阈值,从而完成对固态废弃物渗滤液的污染检测。仿真分析表明,基于小波熵算法的固态废弃物渗滤液的污染检测优化方法精准率高,实用性强。     

基于小波变换的语音增强算法

结合小波技术对传统的维纳滤波算法进行改进,对语音信号进行离散小波变换,求得小波系数,计算小波系数的阈值,然后利用阈值对小波系数进行过滤,再对小波重构信号,信号经过维纳滤波器模型达到去噪效果。最后对算法进行了仿真试验。     

Adaptive fault-tolerant shape control for nonlinear Lipschitz stochastic distribution systems

This paper addresses the problem of adaptive fault estimation and fault-tolerant control for a class of nonlinear non-Gaussian stochastic systems subject to time-varying loss of control effectiveness faults. In this work, time-varying faults, Lipschitz nonlinear property and general stochastic characteristics are taken into consideration in a unified framework. Instead of using the system output signal, the output distribution is adopted for shape control. Both the states and faults are simultaneously estimated by an adaptive observer. Then, a fault tolerant shape controller is designed to compensate for the faults and realize stochastic output distribution tracking. Both the fault estimation and the fault tolerant control schemes are designed based on linear matrix inequality (LMI) technique. Satisfactory performance has been obtained for a numerical simulation example. Furthermore the proposed scheme is successfully tested in a case study of particle size distribution control for an emulsion polymerization reactor.     

Parameter fault detection and estimation of a class of nonlinear systems using observers

The focus of this paper is on the detection and estimation of parameter faults in nonlinear systems with nonlinear fault distribution functions. The novelty of this contribution is that it handles the nonlinear fault distribution function; since such a fault distribution function depends not only on the inputs and outputs of the system but also on unmeasured states, it causes additional complexity in fault estimation. The proposed detection and estimation tool is based on the adaptive observer technique. Under the Lipschitz condition, a fault detection observer and adaptive diagnosis observer are proposed. Then, relaxation of the Lipschitz requirement is proposed and the necessary modification to the diagnostic tool is presented. Finally, the example of a one-wheel model with lumped friction is presented to illustrate the applicability of the proposed diagnosis method.     

Global output feedback tracking control for switched nonlinear systems with deferred prescribed performance

In this paper, the global output feedback tracking control is investigated for a class of switched nonlinear systems with time-varying system fault and deferred prescribed performance. The shifting function is introduced to improve the traditional prescribed performance control technique, remove the constraint condition on the initial value, and make the constraint bounds have more alternative forms. To estimate the unmeasured state variables and compensate the system fault, the switched dynamic gain extended state observer is constructed, which relaxes the traditional Lipschitz conditions on the nonlinear functions. Based on the proposed observer, by constructing the new Lyapunov function and using the backstepping method, the global robust output feedback controller is designed to make the output track the reference signal successfully, and after the adjustment time, the tracking error enters into the prescribed set. The stability of the system is analyzed by the average dwell time method. Finally, simulation results are given to illustrate the effectiveness of the proposed method.     

Fuzzy gain-scheduled active fault-tolerant control of a wind turbine

Advanced fault detection and accommodation schemes are required for ensuring efficient and reliable operation of modern wind turbines. This paper presents a novel approach in designing a fault detection and diagnosis (FDD) and fault-tolerant control (FTC) scheme for a wind turbine using fuzzy modeling, identification and control techniques. First, an improved gain-scheduled proportional-integral (PI) control system based on fuzzy gain scheduling (FGS) technique for multi-input and multi-output wind turbine system is designed. Then, to accommodate sensor faults and based on a signal correction algorithm, an active fault-tolerant control system (AFTCS) is developed as an extension of the gain-scheduled PI control system. The AFTCS exploits the fault information from a model-based FDD scheme developed using fuzzy modeling and identification method. The proposed schemes are evaluated by a series of simulations on a well-known large off-shore wind turbine benchmark in the presence of wind turbulences, measurement noises, and different realistic fault scenarios. All results indicate high effectiveness and robustness of the designed control systems in both fault-free and faulty operations of the wind turbine.     

重载铁路货车轴承突发性损伤类故障检测

重载的铁路货车滚动轴承极易出现损伤类故障,因故障的突发性使得振动信号具有非线性、非平稳的特性,且振动信号极易受非检测部位振动的干扰,而使得有效信号不易被提取,传统的基于傅里叶变换的检测方法无法有效将非平稳的振动信号中的干扰去除,造成最终的检测准确率不高的问题。为了提高检测准确率,提出基于小波包的检测方法,首先针对振动信号的非平稳特性采用小波滤波有效去除其中的干扰,然后通过最小二乘支持向量机提取出非平稳振动信号中的故障特征信号并完成故障检测。实验表明,这种方法能够有效去除振动信号中的干扰,并准确检测重载铁路货车上的滚动轴承损伤故障。     

小波分析在大坝安全监测数据噪声处理中的应用

大坝安全监测数据可以视为通常意义下的时序信号,通过对其进行小波变换分析可有效地进行信噪分离.小波去噪的基本方法有模极大值去嗓、基于各尺度下小波系数相关性进行去噪、阈值去噪、平移不变量小波去噪等方法,本文研究了基干Matlab中小波去噪具体算法并写出了计算程序.实例分析表明.小波分析去嗓能够有效地识别监测数据的噪声,具有操作简单、不失真等优点.     

A process monitoring and fault isolation framework based on variational autoencoders and branch and bound method

Nonlinear characteristic widely exists in industrial processes. Many approaches based on kernel methods and machine learning have been developed for nonlinear process monitoring. However, the fault isolation for nonlinear processes has rarely been studied in previous works. In this paper, a process monitoring and fault isolation framework is proposed for nonlinear processes using variational autoencoder (VAE) model. First, based on the probability graph model of VAE, a uniform monitoring index can be calculated by the probability density of observation variables. Then, the fault variables are estimated with normal variables by a missing value estimation method. The optimal fault variable set can be searched by branch and bound (BAB) algorithm. The proposed method can resolve the ”smearing effects” problem existing in traditional fault isolation methods. Finally, a numerical case and a hot strip mill process case are used to verified the proposed method.     

Fault diagnosis for analog circuits based on chaotic signal excitation

A fault diagnosis method for analog circuit is proposed in this paper. An all-purpose amplifier is taken as the typical circuit under test (CUT). The chaotic signal, which is generated by an improved Chua's circuit, is employed as the excitation signal of CUT. The algorithm for phase-space reconstruction of chaotic time series is a combination algorithm of multiple autocorrelation and Γ-test. The circuit state is estimated based on detecting the geometric change of Chua's attractor with a data-mining approach. For the purpose of information fusion, another eight features are extracted from the testing data to comprehensively determine the CUT states. A discussion is made for comparing the effectiveness of each feature according to the testing results.     

基于心音信号的身份识别方法

提出了一种基于心音信号谱分析的生物识别方法.首先对心音信号进行消噪预处理,然后利用最大熵谱方法提取出心音信号的谱系数特征,采用欧式距离作为匹配算法完成身份识别.实践证明该方法是提取心音信号唯一性特征的有效方法.