一类基于状态估计的非线性系统的智能故障诊断

针对一类含有建模误差的非线性系统,研究了基于状态估计的智能故障诊断方法.首先提出一种状态估计器设计方法;然后在进行状态估计的同时用RBF神经网络来逼近系统所发生的故障.故障估计器的输入为系统的状态估计,所估计出的故障既可用作故障容错控制,也可用作报警.根据微分同胚,将含有建模误差的非线性系统变换为易于分析的规范形式,并在此基础上分析了故障诊断系统的稳定性和鲁棒性.仿真例子证明了该方法的有效性.     

含两类时滞的线性系统的故障诊断及故障可诊断性*

研究同时含有状态时滞和测量时滞的时滞系统的故障诊断方法及故障的可诊断性问题,提出一种同时对状态时滞和测量时滞进行变换的无时滞变换方法,并提出一种新的故障诊断器的构造方法,同时给出时滞系统的故障可诊断性的判据。首先通过提出一种同时对状态时滞和测量时滞进行转换的无时滞转换方法,将时滞系统转换成无时滞的系统;然后将故障诊断问题转换为状态观测问题,给出并证明了故障可诊断性的判据;最后通过构造一种不利用残差体现故障的新的故障诊断器,实现了故障的实时诊断并解决了故障诊断器的物理不可实现问题。仿真实例验证了该方法的可行     

含大时滞和噪声的网络化控制系统的最优故障诊断

研究含大测量时滞和噪声的网络控制系统(Networked control systems, NCS)的故障诊断问题, 提出一种新的基于无时滞转换方法的最优故障诊断器的设计方法. 该方法首先构造一个隐含故障状态的增广系统, 并利用无时滞转换方法将含有测量时滞的网络控制系统转换为无时滞系统. 然后给出了故障的可诊断性判据, 并利用对偶原理将最优故障诊断器的设计问题转换为状态反馈控制器设计问题. 最后, 通过构造一种满足二次型性能指标的最优故障诊断器, 实现了故障的实时诊断. 仿真示例验证了该方法的可行性和有效性.     

基于观测器的离散时滞系统的故障诊断

研究含有测量时滞的线性离散系统的故障诊断问题,提出一种测量时滞的无时滞转换方法和基于降维状态观测器而不利用残差体现故障的故障诊断方法.首先通过构造一个含有故障状态的增广系统和进行测量时滞的无时滞转换,将时滞系统的故障诊断问题转化为无时滞增广系统的状态观测问题;然后给出了其诊断误差能按预先指定的指数速率趋于零的故障诊断器的设计方法.仿真算例验证了该方法的可行性和有效性.     

温度传感器智能故障诊断和容错估计

针对控制系统中经常用到的温度传感器故障检测问题,提出了一种智能故障诊断和容错估计方法。首先,通过充分挖掘其历史故障数据样本及运行过程中的正常历史数据的数据变化规律,建立故障诊断规则库。其次,实时采样相关传感器数据,基于故障样本规则库实现各种故障工况的自动识别,并在故障工况下重构或估计出传感器真实测量值,使系统能够在传感器故障工况下正常运行。最后,通过现场试验验证了所提算法的有效性。     

时滞系统基于对偶原理的故障诊断

研究时滞系统中含有执行器故障和传感器故障情况下的故障诊断方法,提出一种基于对偶原理的故障诊断器的设计方法.首先将故障状态转化为增广系统的状态,然后利用对偶原理将基于观测器的故障诊断器的设计问题转化为状态反馈控制器的设计问题.根据不同的要求,分别设计了渐近稳定的故障诊断器和保性能故障诊断器.利用Lyapunov稳定性理论和线性矩阵不等式技术,实现了系统的实时在线故障诊断.仿真结果证明了该方法的可行性和有效性.     

基于RBF神经网络观测器飞控系统故障诊断

为了解决非线性系统采用解析方法进行故障诊断困难的问题,利用神经网络可逼近任意连续有界非线性函数的能力,提出了一种基于RBF神经网络观测器的故障检测与诊断方法,并详细论述了该故障诊断方法的构造原理。以含有非线性项的飞行控制系统的作动器模型为例,仅作动器的输入输出可测量,通过构造RBF神经网络观测器来拟合作动器系统模型,逼近其在正常情况下的输出。最后在飞控系统的闭环控制环境下,对作动器的三种典型故障进行了计算机仿真诊断,结果表明故障诊断方法是有效的。     

粒子滤波器在移动机器人故障诊断中的应用

针对移动机器人存在的8种不同模式,引入粒子滤波器算法,用于解决移动机器人系统故障诊断问题。基于粒子滤波器的故障诊断算法,通过一组带权值的粒子估计系统状态,计算故障状态的分布情况和故障发生的概率,从而判断是否发生故障以及所发生的故障类型。对粒子滤波器在移动机器人故障诊断中的应用进行仿真实验,并与CMAC神经网络故障诊断方法比较。实验结果表明,采用该方法能有效诊断移动机器人的故障模式,与CMAC神经网络故障诊断方法相比具有优越性。     

不确定线性系统有限时间状态估计与故障重构

针对具有外部干扰和执行器故障的不确定线性系统,给出了一种有限时间内估计系统状态及重构执行器故障的方法．首先,通过状态和输出等价变换,得到不受执行器故障和建模不确定信息干扰的降维解耦系统．在此基础上设计有限时间状态估计器,并设置任意小的时延参数,实现对降维系统状态的有限时间估计,从而达到对原系统状态有限时间估计的目的;其次,考虑高增益滑模微分器对系统输出微分进行有限时间估计;之后,在原系统状态和系统输出微分有限时间估计的基础上,提出一种对系统不确定信息和执行器故障同时估计的方法;最后,通过对具有执行器故障的F-16飞行器纵向系统模型进行仿真,验证所提方法的有效性．     

离散时间线性时变系统的传感器故障估计滤波器设计

针对一类离散时间线性时变系统提出了一种传感器故障诊断方法.本文首先通过状态增广的方式将被研究的系统转化为描述系统的形式,并且基于该描述系统模型,采用方差最小化原则设计了一种能够同时估计系统状态和传感器故障的故障估计滤波器,然后利用一组故障估计滤波器提出了一种故障诊断方法.本文的主要贡献在于针对离散线性时变系统提出了一种不需要对故障动态进行假设的传感器故障诊断方法.所提出方法的另一个优点是该方法能够在存在过程和测量噪声的情况下实现故障检测、分离与估计.仿真结果说明了所提出方法的有效性.     

Fault detection for nonlinear discrete-time systems via deterministic learning

Recently, an approach for the rapid detection of small oscillation faults based on deterministic learning theory was proposed for continuous-time systems. In this paper, a fault detection scheme is proposed for a class of nonlinear discrete-time systems via deterministic learning. By using a discrete-time extension of deterministic learning algorithm, the general fault functions (i.e., the internal dynamics) underlying normal and fault modes of nonlinear discrete-time systems are locally-accurately approximated by discrete-time dynamical radial basis function (RBF) networks. Then, a bank of estimators with the obtained knowledge of system dynamics embedded is constructed, and a set of residuals are obtained and used to measure the differences between the dynamics of the monitored system and the dynamics of the trained systems. A fault detection decision scheme is presented according to the smallest residual principle, i.e., the occurrence of a fault can be detected in a discrete-time setting by comparing the magnitude of residuals. The fault detectability analysis is carried out and the upper bound of detection time is derived. A simulation example is given to illustrate the effectiveness of the proposed scheme.     

基于动态模式的转子系统故障诊断

以Jeffcott转子系统基础松动-碰摩耦合故障为例,研宄动态模式的转子系统故障诊断方法.首先,将转子系统正常和故障时的未知系统动态定义为不同的动态模式,对其进行学习,将学到的知识以常数神经网络权值的形式存储,并建立动态模式库;然后将当前被监测转子系统与动态模式库中的动态模式进行比较,根据动态模式的相似性定义,依据最小误差原则快速判断转子系统与已学过的哪种动态模式相似,实现故障的快速检测与分离.仿真结果验证了算法的有效性.     

Small oscillation fault detection for a class of nonlinear systems with output measurements using deterministic learning

Early detection of small faults is an important issue in the literature of fault diagnosis. In this paper, for a class of nonlinear systems with output measurements, an approach for rapid detection of small oscillation faults is presented. Firstly, locally accurate approximations of unknown system dynamics and fault functions are achieved by combining a high gain observer and a deterministic learning (DL) theory. The obtained knowledge of system dynamics for both normal and fault modes is stored in constant RBF networks. Secondly, a bank of dynamical estimators are constructed for all the normal mode and oscillation faults. The knowledge obtained through DL is reused with a nonhigh-gain design. The occurrence of a fault can be detected if one of residual norms of a fault estimator becomes smaller than that of the normal estimator in a finite time. A rigorous analysis of the detectability properties of the proposed fault detection scheme is also given, which includes the fault detectability condition and the fault detection time. The attractions of the paper lie in that with output measurements, the knowledge of modeling uncertainty and nonlinear faults is obtained and then is utilized to enhance the sensitivity to small faults.     

Distributed sensor fault diagnosis for a formation system with unknown constant time delays

In this paper, a distributed velocity sensor fault diagnosis scheme is presented for a formation of a second-order multi-agent system with unknown constant communication time delays. An existing distributed proportion-derivation (DPD) formation control law is adopted and a delay-independent condition is proposed to guarantee the asymptotical formation stability of the formation system based on the Nyquist stability criterion. Then a distributed fault diagnosis scheme is developed. In each agent, a distributed fault detection residual generator (DFDRG) and a bank of distributed fault isolation residual generators (DFIRGs) are designed based on the closed-loop model of the whole system. Each DFIRG is built up on the basis of a reduced-order unknown input observer (UIO) which is robust to the fault of one neighboring agent. According to the robust relationship between DFIRGs and faults, distributed fault isolation can be achieved. Conditions are presented to guarantee that each agent is able to diagnose faults of itself and its neighbors despite the disturbance of time delays. Finally, outdoor experimental results illustrate the effectiveness of the proposed schemes.     

Sensor fault tolerant control of nonlinear Takagi–Sugeno systems. Application to vehicle lateral dynamics

This paper presents a new scheme for sensor fault tolerant control for nonlinear systems based on the Takagi–Sugeno modeling. First, a structured residual generator aimed at detecting and isolating sensor faults is designed. A bank of observers controlled either by only one system output or a set of outputs is then implemented, leading to a set of state estimates. The parallel distributed compensation structure is adopted to design the fault tolerant controller. The novelty in this paper is that the estimated state used in the controller is a weighted state vector obtained from all the estimated states provided by the different observers. The weighting functions depend on the residual vector signals delivered by the residual generator. They are designed to avoid crisp switches in the control law. Indeed, the interesting feature of the proposed approach is to avoid the commonly used switching strategy. For each residual component, the greater its magnitude is, the less the weight affected to the corresponding state estimate is. Consequently, the controller only uses estimations computed on the basis of healthy measurements. The closed‐loop stability is studied with the Lyapunov theory, and the obtained conditions are expressed as a set of linear matrix inequalities. The proposed residual generation and fault tolerant controller are applied to a vehicle lateral dynamics affected by sensor faults. Copyright © 2015 John Wiley & Sons, Ltd.     

Design of nonlinear observers for detecting faults in hydraulic sub-sea pipelines

Firstly, a general nonlaminar model is considered for pipeline dynamics, including a treatment of faults caused by pipe restrictions. For three cases results are given for stability, robustness and fault detectability of a combined observer and residual (fault detection signal). An efficient numerical design algorithm is proposed. The method is applied to an actual experimental pipeline (rig system) which is set up to model a sub-sea umbilical. Results on modelling and on observer and residual (signal) design are given. The effectiveness of the design is tested by inducing two types of fault on the rig system.     

A robust detection and isolation scheme for abrupt and incipientfaults in nonlinear systems

This paper presents a robust fault diagnosis scheme for abrupt and incipient faults in nonlinear uncertain dynamic systems. A detection and approximation estimator is used for online health monitoring. Once a fault is detected, a bank of isolation estimators is activated for the purpose of fault isolation. A key design issue of the proposed fault isolation scheme is the adaptive residual threshold associated with each isolation estimator. A fault that has occurred can be isolated if the residual associated with the matched isolation estimator remains below its corresponding adaptive threshold, whereas at least one of the components of the residuals associated with all the other estimators exceeds its threshold at some finite time. Based on the class of nonlinear uncertain systems under consideration, an isolation decision scheme is devised and fault isolability conditions are given, characterizing the class of nonlinear faults that are isolable by the robust fault isolation scheme. The nonconservativeness of the fault isolability conditions is illustrated by deriving a subclass of nonlinear systems and of faults for which these conditions are also necessary for fault isolability. Moreover, the analysis of the proposed fault isolation scheme provides rigorous analytical results concerning the fault isolation time. Two simulation examples are given to show the effectiveness of the fault diagnosis methodology     

基于UIOs的风力机传动系统多故障诊断

风力机故障诊断通过对机组运行数据进行提取、估计,以辨别出故障并获得故障信息.但目前风力机故障检测大多考虑单个故障发生的情况,而实际工程中无法避免多故障同时发生.通过设计未知输入观测器组,解决了风力机传动系统执行器和传感器的多故障诊断及隔离.针对不同故障类型各设计一组未知输入观测器.观测器组中的每个未知输入观测器产生一个残差信号,该残差不敏感于相应故障,但敏感于其他故障.通过对比观测器组中的残差信号可实现单一或多故障诊断.建立风力机传动系统故障模型,仿真分析得出该方法具有可行性.     

Unknown Input Fault Detection and Isolation Observer Design for Neutral Systems

This paper deals with actuator fault diagnosis of neutral delayed systems with multiple time delays using an unknown input observer. The main purpose is to design an observer that guarantees the asymptotic stability of the estimate error dynamics and the actuator fault detection. The existence conditions for such an observer are established. The main problem studied in this paper aims at designing observer‐based fault detection and isolation. The designed observer enhances the robust diagnosis performance, including rapidity and accuracy, and generates residuals that enjoy perfect decoupling properties among faults. Based on Lyapunov stability theory, the design of the observer is formulated in terms of linear matrix inequalities, and the diagnosis scheme is based on a bank of unknown input observers for residual generation that guarantees fault detection and isolation in the presence of external disturbances. A numerical example is presented to illustrate the efficiency of the proposed approach.     

A distribution independent data-driven design scheme of optimal dynamic fault detection systems

In this paper, design issues of data-driven optimal dynamic fault detection systems for stochastic linear discrete-time processes are addressed without precise distribution knowledge of unknown inputs and faults. Concerning a family of faults with different distribution profiles in mean and covariance matrix, we introduce a bank of parameter vectors of parity space and construct the parity relation based residual generators using process input and output data. In the context of minimizing the missed detection rate for a prescribed false alarm rate, the design of fault detection system is formulated as a bank of distribution independent optimization problems without posing specific distribution assumption on unknown inputs and faults. It is proven that the optimal selection of individual parameter vector can be formulated as a generalized eigenvalue–eigenvector problem in terms of the means and covariance matrices of residuals in fault-free and each faulty cases, and is thus solved via singular value decomposition. The tight upper bounds of false alarm rate and missed detection rate are simultaneously achieved quantitatively. Besides, the existence condition of the optimal solutions is investigated analytically. Experimental study on a three-tank system illustrates the application of the proposed scheme.