风力机系统基于H_/H∞未知输入观测器的故障诊断

针对实际工程中风力机传动系统未知输入难以完全解耦的问题,提出基于H_-/H_∞未知输入观测器(unknown input observer, UIO)故障诊断方法.首先,利用未知输入观测器的等式条件将未知输入分解为可解耦与不可解耦两部分,且将可解耦部分从估计误差中消除.其次,利用H_-/H_∞性能指标设计未知输入观测器的残差产生器,使残差对未知输入中不可解耦部分具有鲁棒性并对故障信号具有敏感性,并通过引入松弛矩阵解决观测器参数设计过程中的保守问题.考虑残差信号的随机特性,采用统计理论确定故障检测阈值.经仿真验证,该方法可对风力机传动系统传感器加性故障和乘性故障进行有效诊断.     

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.     

故障检测滤波器鲁棒性设计新方法

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,采用参数变化率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,同时结合全维非线性未知输入观测器,利用特征结构配置实现故障检测滤波器对于被控对象模型误差和系统不确定性因素的干扰解耦.通过某型涡扇发动机控制系统传感器故障检测仿真表明,减小残差信号与故障信号之间的误差,提高了残差信号对模型误差和未知输入信号的鲁棒性,对发动机过渡过程中故障检测准确度高,实时性好.     

单区域负荷频率控制系统故障诊断研究

针对一类包含有干扰的负荷频率控制系统,提出一种利用未知输入观测器(UIO,unknown inputobserver)对单区域负荷频率控制系统执行机构进行故障诊断的方法.首先通过构建单区域负荷频率控制系统模型,给出系统的动态方程;其次,通过对系统中的加性未知干扰项进行解耦,构造一个使残差对未知输入具有鲁棒性,而对故障敏感的全阶未知输入观测器,以达到对执行器故障诊断的目的.最后通过Matlab仿真验证了所设计方法的正确性和可行性.     

基于未知输入观测器的导弹间歇故障诊断

针对带有未知扰动和噪声的导弹间歇故障诊断问题,设计了一种基于未知输入观测器的导弹问题故障诊断方法,系统的输入部分或全部未知情况下也能获取系统状态的称为未知输入观测器.首先,为实现对外部扰动的解耦,设计降维未知输入观测器,并通过滑动时间窗口得到对间歇故障敏感而对未知扰动解耦的残差信号;然后,在满足误报率和漏报率的条件下,通过假设检验,确定了间歇故障发生时刻和消失时刻的可检测阈值;最后,对所提出的方法进行了仿真验证.仿真结果表明,在误差允许的范围内,设计的方法能够实现对间歇故障检测,满足实时性和准确性的要求.     

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多智能体一致性控制

现有研究成果大多仅考虑随机噪声或未知输入干扰单一存在的情况,实际工程中两者往往同时存在.在此背景下,本文针对一类含有未知输入干扰和随机噪声的非线性多智能体系统,提出了一种一致性控制协议方法.首先,针对单个智能体系统设计未知输入观测器以消除干扰项对状态估计的影响.参考Kalman滤波器算法来求解状态反馈矩阵,使得输出残差信号的协方差最小,从而增强系统对随机噪声的鲁棒性.然后,基于观测器的状态估计信息,设计了鲁棒一致性控制协议,并将其转化为线性矩阵不等式求解问题.最后,通过一个数值仿真证明了所提方法的正确性和有效性.     

一类伴有部分解耦干扰的非线性系统故障诊断

针对Lipschitz非线性系统执行器故障检测和传感器故障估计问题, 本文提出了一种基于H?/L∞未知输入观测器的有限频域故障诊断策略. 首先, 将系统处理成包含传感器故障的增广系统. 然后, 将该系统的未知输入干扰分为可解耦与不可解耦两部分. 针对可解耦部分, 利用观测器匹配条件将其从估计误差中消除. 针对不可解耦部分,设计L∞指标抑制其对残差的影响并结合有限频域H?指标提高执行器故障检测灵敏度. 接着, 给出观测器存在的充分条件并将其转化为受LMIs约束的线性优化问题, 实现了执行器故障的鲁棒检测及传感器故障的鲁棒估计. 最后, 结合仿真算例验证了所提方法的正确性与有效性.     

基于有限时间未知输入观测器的一类受扰非线性系统故障检测与估计

针对一类非线性系统同时存在执行器故障、传感器故障和扰动的问题,提出一种基于有限时间未知输入观测器的故障检测与估计方法.首先,通过线性非奇异变换将原系统解耦为两个降阶的子系统,其中一个子系统只包含扰动,另一个子系统同时包含扰动和故障;然后,通过一阶低通滤波器获得新的状态并与子系统构成增广系统,实现将原系统的传感器故障转化为增广系统的执行器故障;接着,设计未知输入观测器对增广系统故障进行检测,实现在有限时间内估计出系统的扰动和故障,并通过理论分析验证所设计观测器的有限时间收敛性;最后,基于永磁同步电机(PMSM)转速系统进行仿真研究,仿真结果验证了所提出方法的有效性.     

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。     

Designing fault detection observers for linear systems with mismatched unknown inputs

Algebraic unknown input observers (UIOs) that have been previously reported in the literature can be constructed under the assumption that linear systems with unknown inputs satisfy the so-called observer matching condition. This condition restricts practical applications of UIOs for fault detection and isolation (FDI). We present an algebraic design for fault detection observers (FDOs) for the case in which the observer matching condition is not satisfied. To loosen the restriction imposed by the observer matching condition, the UIO design method combined with the unknown input modeling technique is proposed to design an FDO that decouples the effect of mismatched unknown inputs. To do this, first, unknown inputs that denote the faults of no interest and process disturbances are decomposed into algebraically rejectable unknown inputs and modeled unknown inputs such that the observer matching condition is satisfied. Under the assumption that mismatched unknown inputs are deterministic and can be expressed as the responses of fictitious autonomous dynamical systems, an augmented system is obtained by combining the original system model with the unknown input model. Finally, through the design technique of a UIO for the augmented system, a reduced-order FDO is constructed to estimate an augmented state vector that consists of both the original state variables and the augmentative state variables. The estimated state is then used to generate the residual, which should be designed to be insensitive to unknown inputs while being sensitive to the faults of interest. Two numerical examples are provided to show the usefulness and the feasibility of the presented approach.     

Robust fault diagnosis of disturbed linear systems via a sliding mode high order differentiator

A fault estimator for linear systems affected by disturbances is proposed. Faults appearing explicitly in the state equation and in the system output (actuator faults and sensor faults) are considered. With this design neither the estimation of the state vector nor the estimation of the disturbances is required, implying that the structural conditions are less restrictive than the ones required to design an unknown input observer. Furthermore, the number of unknown inputs (faults plus disturbances) may be greater than the number of outputs. The faults are written as an algebraic expression of a high-order derivative of a function depending on the output. Thus, the reconstruction of the fault signals is carried out by means of a sliding mode high-order differentiator, which requires the derivative of the faults to have a bounded norm.     

Robust Unknown Input Observer Design for Linear Uncertain Time Delay Systems with Application to Fault Detection

In this paper, a novel approach is proposed to design a robust fault detection observer for uncertain linear time delay systems. The system is composed of both norm‐bounded uncertainties and exogenous signals (noise, disturbance, and fault) which are considered to be unknown. The main contribution of this paper is to present unknown input observer (UIO)‐based fault detection system which shows the maximum sensitivity to fault signals and the minimum sensitivity to other signals. Since the system contains uncertainty terms, an H_∞ model‐matching approach is used in design procedure. The reference residual signal generator system is designed so that the fault signal has maximum sensitivity while the exogenous signals have minimum sensitivity on the residual signal. Then, the fault detection system is designed by minimizing the estimation error between the reference residual signal and the UIO residual signal in the sense of H_∞ norm. A sufficient condition for the existence of such a filter is exploited in terms of certain linear matrix inequalities (LMIs). Application of the proposed method in a numerical example and an engineering process are simulated to demonstrate the effectiveness of the proposed algorithm. Simulation results show the validity of the proposed approach to detect the occurrence of faults in the presence of modeling errors, disturbances, and noise.     

Robust sliding-mode observer-based multiple-fault diagnosis scheme

In this study, we simultaneously evaluate the multiple-fault diagnosis problem of a class of Lipschitz nonlinear systems with actuator and sensor faults and unknown input disturbances. A nonsingular system transformation is used to transform the original system into two subsystems for multiple-fault diagnosis: subsystems 1 and 2. At the system level, two robust sliding-mode observers (RSMOs) are proposed. An RSMO is designed for subsystem 1 to detect actuator faults subjected to unknown input disturbances, and another RSMO is designed for subsystem 2 to detect sensor faults subjected to actuator faults. At the component level, a bank of RSMOs is proposed to detect and isolate actuators (sensors) with faults using a dedicated observer scheme. The reachability of RSMOs is comprehensively investigated in the estimation error space. Accordingly, the proposed observer parameters are designed as an optimization problem and solved using the linear matrix inequality (LMI) optimization technique. The effectiveness of the proposed multiple-fault diagnosis scheme was validated through simulations of a modified seventh-order aircraft system.     

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.     

Control‐oriented fault detection of solid oxide fuel cell system unknown input on fuel supply

As the solid oxide fuel cell (SOFC) system work environment is a high‐temperature environment for a long time, it is difficult to obtain the SOFC stack internal state change directly. When the fault occurs, it is difficult to determine where the fault occurs. Moreover, the existing literature ignores the impact of faults, which creates many problems for SOFC system control. Therefore, a state observer‐based fault detection method, which is used to detect the input flow sensor fault and the fuel input fault, is proposed. Their advantage is that they do not need data processing. To realize the fault detection, the observer is used to track the changes of SOFC stack chamber temperature. To obtain the observer estimation parameter, an approach from the actual stack structure parameters is employed to approximate the observer parameters. The results show the proposed fault detect method can judge fuel input fault type quickly and shield the disturbances signals from the sensor effectively. The proposed method also can be used to other operating points or air input fault.     

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。     

Fault detection and estimation for a class of PIDE systems based on boundary observers

In this paper, we propose a simultaneous state estimation and fault estimation approach for a class of first‐order hyperbolic partial integral differential equation systems. Specifically, we consider the multiplicative boundary actuator and sensor faults, ie, unknown fault parameters multiplying by the boundary input or boundary state (ie, output). As a consequence, two difficulties arise immediately: (1) simultaneous estimation of both plant state and faults is a nonlinear problem due to the multiplication between fault parameters and plant signals; (2) no prior information is available to determine the type (actuator or sensor) of faults. To overcome these difficulties, this paper develops adaptive fault parameter update laws and embeds the resulting laws into the plant state observer design. First, we propose new approaches to estimate actuator fault and sensor fault, respectively. Next, we develop a novel method to simultaneously estimate actuator and sensor faults. The proposed observer and update laws, designed using only one boundary measurement, ensure both state estimation and fault parameter estimation. By choosing appropriate Lyapunov functions, we prove that the estimates of state and fault parameters converge to an arbitrarily small neighborhood of their true values. Numerical simulations are used to demonstrate the effectiveness of the proposed estimation approaches.     

Estimation of states,faults and unknown disturbances in non-linear systems

In this paper, we extend existing theory on non-linear unknown input observer design to a wider class of non-linear systems where the systems are subject to unknown input and output disturbances and experience faults. The approach used is to decouple the faults and unknown disturbances from the rest of the system through a series of transformations on state and output equations. Once total disturbance decoupling is achieved, an appropriate observer for the disturbance free part of the non-linear system is proposed and designed. The designed observer is, subsequently, used for estimation of unknown inputs, outputs, and fault signals. Two examples are given for illustration.