Threshold computation for fault detection in linear discrete‐time Markov jump systems

This paper proposes a threshold computation scheme for an observer‐based fault detection (FD) in linear discrete‐time Markovian jump systems. An observer‐based FD scheme typically consists of two stages known as residual generation and residual evaluation. Even information of faults is contained inside a residual signal, a decision of faults occurrence is consequently made by a residual evaluation stage, which consists of residual evaluation function and threshold setting. For this reason, a successful FD strongly depends on a threshold setting for a given residual evaluation function. In this paper, Kalman filter (KF) is used as a residual generator. Based on an accessibility of Markov chain to KF, two types of residual generations are considered, namely mode‐dependent and mode‐independent residual generation. After that threshold is computed in a residual evaluation stage such that a maximum fault detection rate is achieved, for a given false alarm rate. Without any knowledge of a probability density function of residual signal before and after fault occurrence, a threshold is computed by using an estimation of residual evaluation function variance in a fault‐free case. Finally, a detection performance is demonstrated by a numerical example. Copyright © 2013 John Wiley & Sons, Ltd.     

Fault detection filter design for discrete‐time switched linear systems with mode‐dependent average dwell‐time

This paper is concerned with the problem of the fault detection (FD) filter design for discrete‐time switched linear systems with mode‐dependent average dwell‐time. The switching law is mode‐dependent and each subsystem has its own average dwell‐time. The FD filters are designed such that the augmented switched systems are asymptotically stable, and the residual signal generated by the filters achieves a weighted l₂‐gain for some disturbances and guarantees an H _? performance for the fault. By the aid of multiple Lyapunov functions combined with projection lemma, sufficient conditions for the design of the FD filters are formulated by linear matrix inequalities, furthermore, the filters gains are characterized in terms of the solution of a convex optimization problem. Finally, an application to boost convertor is given to illustrate the effectiveness and the applicability of the proposed design method. Copyright © 2013 John Wiley & Sons, Ltd.     

Fault detection for discrete‐time switched singular time‐delay systems: an average dwell time approach

In this paper, the fault detection problem is investigated for a class of discrete‐time switched singular systems with time‐varying state delays. The residual generator is firstly constructed based on a switched filter, and the design of fault detection filter is formulated as an H _∞ filtering problem, that is, minimizing the error between residual and fault in the H _∞ sense. Then, by constructing an appropriate decay‐rate‐dependent piecewise Lyapunov function and using the average dwell time scheme, a sufficient condition for the residual system to be regular, causal, and exponential stable while satisfying a prescribed H _∞ performance is derived in terms of linear matrix inequalities (LMIs). The corresponding solvability condition for the desired fault detection filters is also established via LMI approach. Finally, a numerical example is presented to show the effectiveness of the developed theoretical results.Copyright © 2012 John Wiley & Sons, Ltd.     

Fault detection for a class of network‐based nonlinear systems with communication constraints and random packet dropouts

This paper is dealt with the fault detection (FD) problem for a class of network‐based nonlinear systems with communication constraints and random packet dropouts. The plant is described by a Takagi–Sugeno fuzzy time‐delay model, it has multiple sensors and only one of them is actually communicated with the FD filter at each transmission instant, and the packet dropouts occur randomly. The goal is to design a FD filter such that, for all unknown inputs, control inputs, time delays and incomplete data conditions, the estimation error between the residual and ‘fault’ (or, more generally, the weighted fault) is minimized. By casting the addressed FD problem into an auxiliary H_∞ filtering problem of a stochastic switched fuzzy time‐delay system, a sufficient condition for the existence of the desired FD filter is established in terms of linear matrix inequalities. A numerical example is provided to illustrate the effectiveness and applicability of the proposed technique. Copyright © 2011 John Wiley & Sons, Ltd.     

Fault diagnosis by interval-based adaptive thresholds and peak-to-peak observers

This article investigates actuator fault diagnosis for continuous-time systems with unknown but bounded uncertainties. A novel interval-based adaptive threshold computation approach is proposed for residual evaluation. Meanwhile, peak-to-peak performance is applied to generate robust residuals against system uncertainties. By integrating the designed peak-to-peak residual generator with adaptive thresholds, we can achieve promising fault detection results. Furthermore, based on the general observer scheme, the proposed residual generator and adaptive thresholds can be equally used for fault isolation. Simulation results are given to illustrate the effectiveness and superiority of the proposed fault detection and isolation method.     

Robust multiple‐fault detection and isolation: A gradient flow approach

This paper presents a novel solution to the fault detection and isolation observer design problem for linear time‐invariant systems. A gradient flow approach is proposed for synthesizing a residual generator under optimal eigenstructure assignment. This is achieved by minimizing the spectral condition number of the observer eigenvector matrix. The properties of convergence of the gradient flow solution are proved and its efficiency demonstrated via a numerical example. Copyright © 2007 John Wiley & Sons, Ltd.     

A new kernel RLS algorithm for systems with bounded noise

In this paper, we propose a new nonlinear set‐membership recursive least‐squares algorithm. The algorithm draws on a linear set‐membership filter in conjunction with kernels for nonlinear processing. Set‐membership algorithms exploit a priori model information that directly, or indirectly, prescribes dynamic constraints on the solution space. Such information is disregarded by conventional approaches. Kernel methods provide an implicit mapping of the data in a high‐dimensional feature space where linear techniques are applied. Computations are done in the initial space by means of kernel functions. In this work, we develop a kernel‐based version of a set‐membership filter that belongs to a class of optimal bounding ellipsoid algorithms. Optimal bounding ellipsoid algorithms compute ellipsoidal approximations to regions in the parameter space that are consistent with the observed data and the model assumptions. Experiments involving stationary and nonstationary data are presented. Compared with existing kernel adaptive algorithms, the proposed algorithm offers an enhanced performance and sparsity, conjugated with better tracking capabilities.     

Fault detection for nonlinear networked systems with random packet dropout and probabilistic interval delay

In this paper, the fault detection (FD) problem of nonlinear networked control systems (NCSs) is investigated. A nonlinear stochastic systems model is proposed to account for the NCSs with network‐induced random packet dropout and non‐uniformly distributed time‐varying delay in both from sensor to controller and from controller to actuator. On the basis of the new model, by employing FD filter as residual generator, the addressed FD problem is converted into auxiliary nonlinear H _∞ filtering problem. Then, with the help of Lyapunov functional approach, a sufficient condition for the desired FD filter is constructed in terms of certain linear matrix inequalities, which depends on not only nonlinear level but also delay interval occurrence rate and successful joint packet transmission rate. Especially, a trade‐off phenomenon among maximum allowable delay bound, nonlinear level, and successful joint packet transmission rate is found, which typically resulted from the limited bandwidth of the communication networks. The effectiveness of the proposed methods is demonstrated by simulation examples. Copyright © 2011 John Wiley & Sons, Ltd.     

Convergence analysis of the quasi‐OBE algorithm and related performance issues

‘Quasi‐OBE’ (QOBE) is an adaptive set identification and filtering algorithm which is based on the principles of optimal bounding ellipsoid processing, but which has other geometric and classic least‐squares interpretations which greatly enhance its application potential. In particular, because of its unusual optimization criterion, the ellipsoidal membership set associated with QOBE is more likely to retain (i.e. to move in the parameter space with) the system model's ‘true parameters, ’ say θ _*, when those parameters are time varying. Moreover, in the unlikely event that θ _* moves outside the set, the integrity of the point‐set estimation remains intact, and the estimator provably converges under known conditions. The consistency of the set estimation can be restored at any time using typical ‘rescue procedures’ if desired. Understanding convergence performance is very critical to successful QOBE application. Convergence analysis of both the central point estimate and measures of the hyperellipsoidal membership set is presented. The main results give conditions for point estimate convergence, and show that set convergence to a point is not possible. Implications of these convergence results for practical application are discussed. Copyright © 2007 John Wiley & Sons, Ltd.     

Adaptive threshold generation in robust fault detection using interval models: time‐domain and frequency‐domain approaches

In this paper, robust fault detection is addressed on the basis of evaluating the residual energy that it is compared against worst‐case value (threshold) generated considering parametric modeling uncertainty using interval models. The evaluation of the residual/threshold energy can be performed either in the time or frequency domain. This paper proposes methods to compute such energy in the two domains. The first method generates the adaptive threshold in the time domain through determining the worst‐case time evolution of the residual energy using a zonotope‐based algorithm. The second method evaluates the worst‐case energy evolution in the frequency domain using the Kharitonov polynomials. Results obtained using both approaches are related through the Parseval's theorem. Finally, two application examples (a smart servoactuator and a two DOFs helicopter) will be used to assess the validity of the proposed approaches and compare the results obtained. Copyright © 2012 John Wiley & Sons, Ltd.     

Fault detection filter design for networked control systems with time‐varying sampling periods and packet dropouts

This paper investigates the problem of fault detection for networked control systems under simultaneous consideration of time‐varying sampling periods and packet dropouts. By taking time‐varying sampling periods into consideration, a new closed‐loop model for the considered networked control systems is established. The sampling period switching‐based approach and the parameter uncertainty‐based approach are adopted to deal with time‐varying sampling periods. Based on the established model, the observer‐based fault detection filter design criteria are proposed to asymptotically stabilize the residual system in the sense of mean‐square. The designed observer‐based fault detection filter can guarantee the sensitivity of the residual signal to faults. The simulation results illustrate the effectiveness of the obtain results. Copyright © 2015 John Wiley & Sons, Ltd.     

Network‐based fault detection for discrete‐time state‐delay systems: A new measurement model

In this paper, the fault detection problem is studied for a class of discrete‐time networked systems with multiple state delays and unknown input. A new measurement model is proposed to account for both the random measurement delays and the stochastic data missing (package dropout) phenomenon, which are typically resulted from the limited capacity of the communication networks. At any time point, one of the following cases (random events) occurs: measurement missing case, no time‐delay case, one‐step delay case, two‐step delay case, …, q‐step delay case. The probabilistic switching between different cases is assumed to obey a homogeneous Markovian chain. We aim to design a fault detection filter such that, for all unknown input and incomplete measurements, the error between the residual and weighted faults is made as small as possible. The addressed fault detection problem is first converted into an auxiliary H_∞ filtering problem for a certain Markovian jumping system (MJS). Then, with the help of the bounded real lemma of MJSs, a sufficient condition for the existence of the desired fault detection filter is established in terms of a set of linear matrix inequalities (LMIs). A simulation example is provided to illustrate the effectiveness and applicability of the proposed techniques. Copyright © 2007 John Wiley & Sons, Ltd.     

Set theoretic performance verification of low‐frequency learning adaptive controllers

Although adaptive control has been used in numerous applications, the ability to obtain a predictable transient and steady‐state closed‐loop performance is still a challenging problem from the verification and validation standpoint. To that end, we considered a recently developed robust adaptive control methodology called low‐frequency learning adaptive control and utilized a set of theoretic analysis to show that the transitory performance of this approach can be expressed, analyzed, and optimized via a convex optimization problem based on linear matrix inequalities. This key feature of this design and analysis framework allows one to tune the adaptive control parameters rigorously so that the tracking error components of the closed‐loop nonlinear system evolve in a priori specified region of the state space whose size can be minimized by selecting a suitable cost function. Simulation examples are provided to demonstrate the efficacy of the proposed verification and validation architecture showing the possibility of performing parametric studies to analyze the interplay between the size of the tracking error residual set and important design parameters such as the adaptation rate and the low‐pass filters time constant of the weights adaptation algorithm. Copyright © 2014 John Wiley & Sons, Ltd.     

Active fault‐tolerant attitude control for flexible spacecraft with loss of actuator effectiveness

A theoretical framework for active fault‐tolerant attitude stabilization control is developed and applied to flexible spacecraft. The proposed scheme solves a difficult problem of fault‐tolerant controller design in the presence of severe partial loss of actuator effectiveness faults and external disturbances. This is accomplished by developing an observer‐based fault detection and diagnosis mechanism to reconstruct the actuator faults. Accordingly, a backstepping‐based fault‐tolerant control law is reconfigured using the reconstructed fault information. It is shown that the proposed design approach guarantees that all of the signals of the closed‐loop system are uniformly ultimately bounded. The closed‐loop performance of the proposed control strategy is evaluated extensively through numerical simulations. Copyright © 2012 John Wiley & Sons, Ltd.     

大型发电机的多特征参量故障发生概率评定方法(英文)

It is conducive to ensure the safety and reliability of the power system by means of timely and accurate evaluation of the reliability of large generators.An evaluation method of fault probability for large generators based on multi-characteristic parameters is proposed in this paper,which can be employed to solve the difficult problem of evaluating the occurrence probability of fault modes of large generators in the process of reliability evaluation.Firstly,the set of fault characteristic parameters for a large generator is established on the basis of a large number of operation experiences and preventive test code for the large generator.Secondly,according to the features of the fault characteristic parameters,two estimation methods of the fault mode occurrence probability are proposed,which are based on the fuzzy membership function and the relative deterioration degree,respectively.Finally,on the basis of the improved analytic hierarchy process(IAHP) method,the composite estimation method of the fault probability for a large generator is presented,which can reasonably and adequately use the information of various characteristic parameters.An assessment case is provided to illustrate the specific process of this evaluation method and verify its effectiveness.     

Impacts of DFIG‐based wind farm integration on its tie line distance protection and countermeasures

Modern power grids, especially high‐voltage grids, are increasingly integrated with wind farms. The correct action of distance protection for the wind farm tie line plays an important role in the safe and stable operation of the power system. However, based on the fault characteristics of wind farms based on the doubly fed induction generator, considering crowbar action and weak infeed properties, this paper reveals some serious defects of conventional distance protection. The result shows that, due to the influences of mixed data window and damped current component with near‐rotor‐speed frequency, the fault distance calculation result obtained by the fundamental‐component‐based distance protection is inaccurate. To tackle this problem, a transient‐based distance protection countermeasure along with a faulted phase selection technique is proposed. Specifically, the transient phase voltage characteristic for different fault types is analyzed to select the faulted phase. Considering the effect of high‐frequency components, pre‐fault voltage compensation, and transducer characteristics, a distance calculation procedure including a low‐pass filter, the reconstruction of voltage at the fault point, and an iterative fault distance calculation is proposed. The comprehensive performance of the proposed method is validated by the simulation case studies using the PSCAD /EMTDC software. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

A robust fixed‐lag receding horizon smoother for uncertain state space models

In this paper, a robust fixed‐lag receding horizon (RH) smoother is proposed for a discrete‐time state space model with bounded uncertainties. The proposed robust RH smoother is based on a set‐valued state estimation method, which produces a set of possible estimates based on inputs and outputs measured over a finite time horizon. The set of possible estimates is represented in an ellipsoidal form, and then the major axis of the ellipsoid is minimized with respect to the preview size. The center of the ellipsoid with the minimum major axis is taken as the estimate of the real state to minimize the possible maximum estimation error under bounded uncertainties. It is illustrated through a numerical example that the proposed robust RH smoother has better performance than the conventional robust Kalman smoothers. Copyright © 2015 John Wiley & Sons, Ltd.