采样数据系统故障检测的H∞方法

提出一种对采样数据系统进行故障检测的H∞方法.首先构造离散时间故障检测系统,分析残差产生器的动态特性.然后从故障检测的角度引入算子,定量考察连续时间未知输入信号和故障信号对离散时间残差信号的影响.最后优化残差产生器的参数,使得故障检测系统对未知输入具有较强的鲁棒性,同时对故障具有较高的灵敏度.     

基于H_/H∞未知输入观测器的线性切换系统故障检测

利用H_/H_∞未知输入观测器方法研究线性切换系统的故障检测问题.将未知输入干扰分解为可解耦和不可解耦两部分,从估计误差中消除可解耦部分,再使用加权H_/H_∞混合性能指标表示剩余未知输入和故障对残差的影响,将故障检测问题等价为加权H_/H_∞未知输入观测器设计问题.根据平均驻留时间技术和多Lyapunov函数方法设计了故障检测器参数,并利用松弛矩阵方法得到了进一步的结果.通过一个仿真例子对所提出方法进行了验证.     

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.     

广义双线性系统的残差产生器设计

针对具有不可测外界干扰和有界控制输入的广义双线性系统,提出了一种用于故障检测的未知输入残差产生器的设计方法。首先,通过非奇异线性变换,将广义双线性系统分解成动态和静态两个子系统,设计了未知输入残差产生器,给出了未知输入残差产生器存在的充分必要条件,并基于Lyapunov稳定理论,分析了所设计的未知输入残差产生器的指数稳定性。然后,针对未知输入残差产生器中的待定参数,基于线性矩阵不等式技术和广义逆理论,给出了未知参数的求解过程,该求解方法不需要对待求参数进行参数化的过程,易于计算。     

模型不确定性线性系统的鲁棒故障检测滤波器设计

基于给定的性能指标函数, 将受模型不确定性和范数有界未知输入影响的线性不确定系统的基于观测器的鲁棒故障检测滤波器设计问题归结为H_∞ 优化问题, 并通过选取适当的后置滤波器和观测器增益矩阵, 使产生的残差达到对于未知输入和模型不确定性的鲁棒性与对于故障的灵敏度的最佳均衡. 简例验证了本文提出算法的有效性.     

采样数据系统的故障检测

研究了采样数据系统的故障检测问题.关键在于通过引入算子来分别描述连续时间的 未知输入信号和故障信号对离散时间的残差信号的影响,从而把鲁棒性和灵敏度问题定义为一 个优化问题,并给出了相应的解.仿真例子验证了该方法的有效性.     

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.     

基于输出时滞方法的非均匀采样数据系统传感器故障检测(英文)

研究一般非均匀采样数据系统鲁棒传感器故障检测设计问题.首先,基于输出时滞方法将非均匀采样数据系统转换成具有时变时滞输出的连续系统;然后,选择故障检测滤波器作为残差产生器,并将故障检测设计问题描述成一个多目标优化问题,即连续时间过程噪声和离散时间测量噪声对残差信号的H∞范数小于一个给定值,同时传感器故障对残差信号的l2增益大于一个给定值,基于输入输出方法以矩阵不等式的形式给出该多目标优化问题有解的充分条件;进一步的,提出一个迭代算法来权衡噪声鲁棒性与故障灵敏度,并将矩阵不等式转换成可解的线性矩阵不等式.最后,对某型飞控系统的仿真实验验证了所提方法的有效性.     

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.     

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.     

Robust Fault Detection for Switched Linear Systems With State Delays

This correspondence deals with the problem of robust fault detection for discrete-time switched systems with state delays under an arbitrary switching signal. The fault detection filter is used as the residual generator, in which the filter parameters are dependent on the system mode. Attention is focused on designing the robust fault detection filter such that, for unknown inputs, control inputs, and model uncertainties, the estimation error between the residuals and faults is minimized. The problem of robust fault detection is converted into an H _infin-filtering problem. By a switched Lyapunov functional approach, a sufficient condition for the solvability of this problem is established in terms of linear matrix inequalities. A numerical example is provided to demonstrate the effectiveness of the proposed method.     

Fault detection for linear discrete-time systems with output quantisation

This paper is concerned with the problem of fault detection for a class of linear discrete-time quantised feedback systems, and system outputs are, respectively, quantised by a uniform quantiser and a non-uniform quantiser. A novel unified method is proposed to deal with the two different types of quantisation errors, i.e. the quantisation errors are considered as unknown inputs of the residual generator. Then, an optimal residual generator is constructed to guarantee a suitable trade-off between the sensitivity to faults and the robustness against disturbances as well as quantisation errors. In order to detect the occurrence of faults, the generated residual signal is evaluated, and then two different thresholds based on the stochastic properties of quantisation errors are designed for these two quantisers. In addition, the false alarm rate is introduced to evaluate the fault detection performance. Finally, two examples are given to illustrate the feasibility of the proposed approach.     

Robust fault detection for discrete-time Markovian jump systems with mode-dependent time-delays

This paper investigates a fault detection problem for a class of discrete-time Markovian jump systems with norm-bounded uncertainties and mode-dependent time-delays. Attention is focused on constructing the residual generator based on the filter which its parameters matrices are dependent on the system mode, that is, the fault detection filter is a Markovian jump system as well. The design of fault detection filter is reduced to H-infinity filtering problem by using H-infinity control theory, which can guarantee the difference between the residual and the fault (or, more generally weighted fault) as small as possible in the context of enhancing the robustness of residual to modeling errors, control inputs and unknown inputs. Sufficient condition for the existence of the above filters is established by means of linear matrix inequalities, which can be readily {solved by using standard numerical software. A numerical example is given to illustrate the feasibility of the proposed method.     

Robust fault detection for discrete-time Markovian jump systems with mode-dependent time-delays

This paper investigates a fault detection problem for a class of discrete-time Markovian jump systems with norm-bounded uncertainties and mode-dependent time-delays. Attention is focused on constructing the residual generator based on the filter of which its parameters matrices are dependent on the system mode, that is, the fault detection filter is a Markovian jump system as well. The design of fault detection filter is reduced to H-infinity filtering problem by using H-infinity control theory, which can guarantee the difference between the residual and the fault (or, more generally weighted fault) as small as possible in the context of enhancing the robustness of residual to modeling errors, control inputs and unknown inputs. Sufficient condition for the existence of the above filters is established by means of linear matrix inequalities, which can be readily solved by using standard numerical software. A numerical example is given to illustrate the feasibility of the proposed method.     

具有随机马尔可夫时延的网络控制系统H∞故障检测

针对存在随机时延的网络环境,研究了一类网络控制系统的H∞故障检测滤波器设计问题．考虑了可 由马尔可夫过程描述的随机时延,网络控制系统被建模成离散时间有限状态的马尔可夫跳变线性系统．基于此类模 型,构造了系统的残差发生器,相应的故障检测问题转化为H∞滤波问题．通过选择合适的滤波器增益,使得残差和 故障之间的误差尽可能地小,保证了残差对于未知输入的鲁棒性．以线性矩阵不等式的形式给出了故障检测滤波器 存在的充分条件,并运用锥补线性化方法完成了滤波器设计过程．数值算例表明,上述网络控制系统故障检测滤波 器不仅对故障敏感,同时对外界扰动具有鲁棒性．     

H ∞ fault detection filter design for linear discrete-time systems with multiple time delays

This paper deals with the design of H^∞ fault detection filters for discrete-time systems with multiple time delays, in which total decoupling of the fault effects from unknown inputs, including model uncertainties and external plant disturbances, is impossible. Through the appropriate choice of the filter gain, the filter is convergent if there is no fault in the system, and the effect of disturbances on the residual is minimized in the sense of H^∞ norm. The problem of achieving satisfactory sensitivity of the residual to faults is formulated and its solution given. The detection threshold associated with the filter is also discussed. Finally, a wind tunnel example illustrates the efficiency of the proposed method.     

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.     

UIO design for singular delayed LPV systems with application to actuator fault detection and isolation

In this paper, the unknown input observer (UIO) design for singular delayed linear parameter varying (LPV) systems is considered regarding its application to actuator fault detection and isolation. The design procedure assumes that the LPV system is represented in the polytopic framework. Existence and convergence conditions for the UIO are established. The design procedure is formulated by means of linear matrix inequalities (LMIs). Actuator fault detection and isolation is based on using the UIO approach for designing a residual generator that is completely decoupled from unknown inputs and exclusively sensitive to faults. Fault isolation is addressed considering two different strategies: dedicated and generalised bank of observers’ schemes. The applicability of these two schemes for the fault isolation is discussed. An open flow canal system is considered as a case study to illustrate the performance and usefulness of the proposed fault detection and isolation method in different fault scenarios.     

Data-driven output-feedback fault-tolerant control for unknown dynamic systems with faults changing system dynamics

This paper studies the data-driven output-feedback fault-tolerant control (FTC) problem for unknown dynamic systems with faults changing system dynamics. In a framework of active FTC, two basic issues are addressed: the fault detection employing only the measured input–output information; the controller reconfiguration to achieve optimal output-feedback control in the presence of multiple faults. To detect faults and write the system state via the input–output data, an approach to data-driven design of a residual generator with a full-rank transformation matrix is presented. An output-feedback approximate dynamic programming method is developed to solve the optimal control problem under the condition that the unknown linear time-invariant discrete-time plant has multiple outputs. According to the above results and the proposed input–output data-based value function approximation structure of time-varying plants, a model-free output-feedback FTC scheme considering optimal performance is given. Finally, two numerical examples and a practical example of a DC motor control system are used to demonstrate the effectiveness of the proposed methods.