基于广义未知输入观测器的鲁棒故障估计方法

当干扰存在时,有效地估计故障且放松故障的限制条件需要进一步的研究,为此针对含未知干扰的非线性连续系统的鲁棒故障估计问题提出一种广义未知输入观测器方法。首先,将执行器故障向量和传感器故障向量与原系统状态向量组成广义系统,放松对故障类型的限制,对此广义系统设计未知输入观测器解耦干扰,保证鲁棒性的同时估计出状态变量、执行器故障及其一阶微分和传感器故障。然后通过解线性矩阵不等式（LMI）给出估计误差渐近收敛的条件。最后,在MATLAB 的simulink平台上用三叶片水平轴风力模型仿真验证本文观测器的故障估计有效性鲁棒性。     

一类存在参数摄动的线性随机系统的鲁棒间歇故障诊断方法

间歇故障（Intermittent faults, IFs）具有随机性,其检测要求在本次间歇故障消失之前检测出间歇故障的发生,在下一次间歇故障发生之前检测出间歇故障的消失.本文针对一类存在未知时变参数摄动的离散线性随机动态系统,研究了其鲁棒间歇故障检测与分离问题.基于降维未知输入观测器,通过引入滑动时间窗口,本文设计了一组与未知时变摄动解耦的结构化截断残差,并提出其存在的一个充分条件.与传统残差相比,截断残差信号更为显著地反映了间歇故障的发生和消失.为满足间歇故障的检测要求,本文提出两个假设检验分别用于检测间歇故障的发生时刻和消失时刻,并给出了一个详细算法.最后,在沿参考轨道运行的卫星模型上对所述方法进行了仿真实验,结果表明该方法能够有效检测出间歇故障的所有发生时刻和消失时刻,并准确实现故障分离.     

含未知输入观测器的广义系统故障诊断

基于未知输入观测器设计和故障诊断的概念,讨论含未知输入的Lipschitz条件下非线性广义系统传感器故障诊断问题。在非线性广义系统中,通过引入传感器的故障信号,重新构造非线性广义系统,设计基于未知输入观测器,在满足Lipschitz条件下,实现了传感器故障的检测与分离。给出数值仿真算例验证该算法的有效性。     

基于未知输入观测器的非线性系统故障诊断

针对一类含未知输入和执行器故障的非线性系统,提出基于未知输入观测器的故障诊断算法,改进了Luenberger故障诊断观测器对系统出现未知扰动时的不足.利用广义逆方法,将未知输入从残差信号中完全解耦,通过产生对故障高敏感性以及对未知扰动强抗扰动性的观测器实现系统的故障诊断,并通过Lyapunov函数用线性矩阵不等式保证了系统稳定性.     

基于未知输入观测器的非线性时间序列故障预报

将未知非线性系统的输出作为时间序列并进行空间重构,针对得到的离散线性时变系统,提出了基于未知输入观测器的预测新方法.以实时拟合时间序列的线性AR模型作为时变系统的已知线性部分,将拟合误差作为时变系统的未知输入,实现了对非线性时间序列的一步预测.再利用递推预测的方法,将一步预测推广到N步预测,同时证明了该方法的预测误差有界.通过未知输入的预测值和状态的预测误差的变化可以方便地判断故障的发生,实现故障预报.仿真结果证明了方法的有效性.     

一种基于最优未知输入观测器的故障诊断方法

针对含有未知输入干扰和噪音的不确定动态系统,使用全阶未知输入观测器(Unknown input observer, UIO)来消除干扰项,实现状态估计, 结合Kalman滤波器算法来求解状态反馈矩阵,以使得输出残差信号的协方差最小,从而增强系统对噪声的鲁棒性,实现了 一种基于最优未知输入观测器的残差产生器.采用极大似然比(Generalized likelihood ratio, GLR)的方法对残差信号进行评估,通过设定的阈值来提高诊断率. 最后以风力发电机组传动系统出现加性传感器故障和乘性传感器故障为例, 进行了残差信号的仿真,仿真结果说明了该方法的有效性.     

基于未知输入观测器的直流电机鲁棒故障估计

针对直流电机系统提出一种基于未知输入观测器(UIO)的鲁棒故障估计方法,同时估计系统中的执行器故障和传感器故障。首先,构建包含系统传感器故障的增广状态系统;然后,基于该增广系统提出一种新颖的UIO,并给出了该观测器的存在条件和多故障估计策略;同时,引入H_∞性能指标最大程度地抑制干扰对故障估计结果的影响;接着,给出观测器的设计条件和参数求解过程并将其转化为易于求解的线性矩阵不等式(LMI)的形式;最后,通过算例仿真和实验验证了该方法的有效性和可用性。     

匹配条件不满足时线性系统未知输入观测器设计

针对一类不满足观测器匹配条件的线性系统,讨论了未知输入观测器设计方法.首先,为了突破观测器匹配条件的限制,提出了一种与未知输入相对阶无关的辅助输出构造方法.然后,把未知输入看作系统状态的一部分,将原系统转化为一个不含未知输入的增维线性描述系统.针对这样的系统转化,对一系列等价前提条件进行了详细的讨论.之后,针对该增维线性描述系统,构造Luenberger观测器来估计原系统的状态和未知输入.同时,借助于高阶滑模微分器,来估计辅助输出中的未知信号.最后,对一个单连杆柔性机械手模型进行了数值仿真,仿真结果表明了方法的有效性.     

基于自适应未知输入观测器的非线性动态系统故障诊断

针对以往故障诊断研究中要求故障或故障导数及系统干扰的上界是已知的不足,以及难以同时诊断执行器故障和传感器故障的问题,提出一种自适应未知输入故障诊断观测器,能够同时重构非线性动态系统的执行器故障和传感器故障.首先,利用H_∞性能指标抑制未知输入对故障重构的影响,采用Lyapunov泛函得到观测误差动态系统的稳定性;然后,通过线性矩阵不等式求解观测器增益阵,并实现故障重构;最后,通过直流电机系统的仿真验证了所提出方法的有效性.     

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.     

基于未知输入观测器的非线性广义系统执行器故障检测和重构

针对一类含有未知扰动广义非线性系统的执行器故障,本文提出一种重构算法。首先设计未知输入观测器对干扰鲁棒,作为故障检测观测器。检测到发生故障后,通过提出含有误差比例项和积分项的故障估计算法,形成自适应观测器,实现准确快速地估计故障,同时估计状态变量。根据李雅普诺夫稳定理论给出估计误差一致最终有界的充分条件。最后仿真验证该类观测器和重构算法的有效性。     

一种基于UIO观测器的故障检测方法

本文基于未知输入观测器，针对含有未知输入干扰的不确定动态系统，提出了一种故障检测的新方法。本文提出的方法与其它方法如特征向量配置法相比，具有条件要求较低，设计简单的优点。     

Diagnosis of Intermittent Faults

The diagnosis of intermittent faults in dynamic systems modeled as discrete event systems is considered. In many systems, faulty behavior often occurs intermittently, with fault events followed by corresponding reset events for these faults, followed by new occurrences of fault events, and so forth. Since these events are usually unobservable, it is necessary to develop diagnostic methodologies for intermittent faults. Prior methodologies for detection and isolation of permanent faults are no longer adequate in the context of intermittent faults, since they do not account explicitly for the dynamic behavior of these faults. This paper addresses this issue by: (i) proposing a modeling methodology for discrete event systems with intermittent faults; (ii) introducing new notions of diagnosability associated with fault and reset events; and (iii) developing necessary and sufficient conditions, in terms of the system model and the set of observable events, for these notions of diagnosability. The definitions of diagnosability are complementary and capture desired objectives regarding the detection and identification of faults, resets, and the current system status (namely, is the fault present or absent). The associated necessary and sufficient conditions are based upon the technique of diagnosers introduced in earlier work, albeit the structure of the diagnosers needs to be enhanced to capture the dynamic nature of faults in the system model. The diagnosability conditions are verifiable in polynomial time in the number of states of the diagnosers.     

Fault Detection and Isolation Method Based on H−/H∞ Unknown Input Observer Design in Finite Frequency Domain

This paper proposes an actuator fault detection and isolation strategy based on a bank of unknown input observers with finite frequency specifications. In order to deal with actuator fault diagnosis problem, a bank of H _?/H _∞ unknown input observers are designed to generate residuals, which are insensitive to the corresponding faults but sensitive to the other actuators faults, and meanwhile robust against the unknown disturbances. In this paper, the actuator faults and unknown disturbances are considered to belong to finite frequency domains, and two finite frequency performance indices are used to measure the fault sensitivity and the disturbance robustness of the residuals. Furthermore, some parameters for extra design of freedom are introduced in the H _?/H _∞ unknown input observers design. Based on the generalised Kalman‐Yakubovich‐Popov (GKYP) lemma, the design conditions of the H _?/H _∞ unknown input observer are derived and formulated as linear matrix inequalities (LMIs). Finally, a VTOL aircraft model is used to demonstrate the performance of the proposed fault diagnosis scheme.     

Fault Detection for Nonlinear Systems with Unknown Input

This paper extends the problem of fault detection for linear discrete‐time systems with unknown input to the nonlinear system. A nonlinear recursive filter is developed where the estimation of the state and the input are interconnected. Unknown input which can be any type of signal was obtained by least‐squares unbiased estimation and the state estimation problem is transformed into a standard unscented Kalman filter (UKF) problem. By testing the mean of the innovation process, a real‐time fault detection approach is proposed. Simulations are provided to demonstrate the effectiveness of the theoretical results.