Fault Detection for Uncertain Fuzzy Systems Based on the Delta Operator Approach

This paper investigates the problem of designing a robust fault-detection for uncertain T-S fuzzy models based on the delta operator approach. By means of the T-S fuzzy delta operator systems, a fuzzy fault detection filter system is constructed via the delta operator approach. The worst case fault sensitivity has been formulated in terms of linear matrix inequalities. The proposed fault-detection filter not only ensures the H _?-gain from a fault signal to a residual signal greater than a prescribed value, but also guarantees the H _∞-gain from an exogenous input to a residual signal less than a prescribed value in terms of the solvability of linear matrix inequalities. The linear matrix inequalities can be solved by an effective algorithm. A numerical example is provided to illustrate the effectiveness of the proposed design techniques.     

Robust Fault Detection of Uncertain Time-Delay Markovian Jump Systems with Different System Modes

In this paper, a robust fault detection filter (RFDF)design scheme is presented for uncertain nonlinear Markovian jump systems with mixed delays. By using a observer-based fault detection filter as residual generator, the RFDF design is formulated as an H _∞-filtering problem. Particularly, two different Markov processes are considered for modeling the randomness of system matrix and the state delay; meanwhile, the corresponding two kinds of transition rate matrices are assumed to be incompletely accessible, that is, the one with partially unknown entries and the other with polytopic uncertainties. By using a new convex polyhedron technique, some new sufficient conditions are established in terms of delay-dependent linear matrix inequalities (LMIs) to synthesize the residual generation scheme. Finally, a numerical example is given to illustrate the effectiveness of the proposed techniques.     

Reliable Fault Detection for Nonlinear Networked Systems with Imperfect Measurements: A Multi-Packet Transmission Mechanism

In this paper, the reliable fault detection problem is investigated for a class of nonlinear networked systems with imperfect measurements. A novel measurement model is proposed to take the network-induced delays, random packet dropouts and sensor failures into consideration simultaneously. Different from commonly used single-packet transmission pattern, a multi-packet transmission mechanism is adopted in the sensor-to-filter channel, which allows the measured outputs being encapsulated into multiple packets and transmitted to the remote fault detection filter via different communication channels. In these channels, the network status may be quite different from each other. We aim to design a reliable fault detection filter such that, for all admissible unknown inputs and imperfect measurements, the error between the residual and the fault is kept as small as possible. By constructing an appropriate Lyapunov–Krasovskii functional, a sufficient condition for the existence of the desired reliable fault detection filter is established in terms of linear matrix inequalities, and the corresponding optimal filter parameters can be obtained by solving a convex optimization problem. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method.     

Delay-dependent fault detection for switched linear systems with time-varying delays—the average dwell time approach

In this paper, the fault detection problem is investigated for a class of discrete-time switched linear systems with time-varying delays. The main purpose is to design a fault detection filter such that, for all unknown inputs, control inputs and time delays, the estimation error between the residual and fault is minimized in an exponential way. The fault detection problem is converted into an exponential H_∞ filtering problem. By using a newly constructed Lyapunov functional and the average dwell time scheme, a novel delay-dependent sufficient condition for the solvability of this problem is established in terms of linear matrix inequalities (LMIs). A numerical example is given to demonstrate the effectiveness of the developed theoretical results.     

On design of quantized fault detection filters with randomly occurring nonlinearities and mixed time-delays

This paper is concerned with the fault detection problem for a class of discrete-time systems with randomly occurring nonlinearities, mixed stochastic time-delays as well as measurement quantizations. The nonlinearities are assumed to occur in a random way. The mixed time-delays comprise both the multiple discrete time-delays and the infinite distributed delays that occur in a random way as well. A sequence of stochastic variables is introduced to govern the random occurrences of the nonlinearities, discrete time-delays and distributed time-delays, where all the stochastic variables are mutually independent but obey the Bernoulli distribution. The main purpose of this paper is to design a fault detection filter such that, in the presence of measurement quantization, the overall fault detection dynamics is exponentially stable in the mean square and, at the same time, the error between the residual signal and the fault signal is made as small as possible. Sufficient conditions are first established via intensive stochastic analysis for the existence of the desired fault detection filters, and then the explicit expression of the desired filter gains is derived by means of the feasibility of certain matrix inequalities. Also, the optimal performance index for the addressed fault detection problem can be obtained by solving an auxiliary convex optimization problem. A practical example is provided to show the usefulness and effectiveness of the proposed design method.     

Fault Detection for Networked Nonlinear Systems with Time Delays and Packet Dropouts

This paper addresses the problem of fault detection (FD) for networked systems with global Lipschitz nonlinearities and incomplete measurements, including time delays and packet dropouts which are described by a more general model using Markov jump system approach. We aim to design a mode-dependent fault detection filter (FDF) such that, for all external disturbances and incomplete measurements, the error between the residual and fault is made as small as possible. The addressed FD problem is then converted into an auxiliary H _∞ filtering problem of Markov jump system with time-varying delay. By applying Lyapunov–Krasovskii approach, a sufficient condition for the existence of the FDF is derived in terms of certain linear matrix inequalities (LMIs). When these LMIs are feasible, the explicit expression of the desired FDF can also be characterized. A numerical example is exploited to show the effectiveness of the results obtained.     

Data-driven design of robust fault detection system for wind turbines

In this paper, a robust data-driven fault detection approach is proposed with application to a wind turbine benchmark. The main challenges of the wind turbine fault detection lie in its nonlinearity, unknown disturbances as well as significant measurement noise. To overcome these difficulties, a data-driven fault detection scheme is proposed with robust residual generators directly constructed from available process data. A performance index and an optimization criterion are proposed to achieve the robustness of the residual signals related to the disturbances. For the residual evaluation, a proper evaluation approach as well as a suitable decision logic is given to make a correct final decision. The effectiveness of the proposed approach is finally illustrated by simulations on the wind turbine benchmark model.     

一种新的多速率采样系统快速率 故障检测设计方法

针对一类具有随机干扰的多速率采样系统,本文建立了一种快速率故障检测方法.首先,应用提升和初等变换等技术将原有系统转换为线性时不变(LTI)且满足因果约束的慢速率系统;然后应用Kalman滤波设计出系统的慢速率稳态残差产生器;同时,为了克服上述残差产生器不能实时检测故障的缺陷,并考虑新系统中所增加的过程噪声与测量噪声之间、各测量噪声之间相关性等因素,基于序贯滤波的思想,进一步设计出能实时检测故障且能避免因果约束问题的快速率稳态残差产生器和残差评估方法;最后,用计算机仿真对所提方法的有效性进行了验证.     

Robust filtering for discrete-time systems with saturation and its application to transmultiplexers

This paper considers the problem of robust filtering for discrete-time linear systems subject to saturation. A generalized dynamic filter architecture is proposed, and a filter design method is developed. Our approach incorporates the conventional linear H/sub 2/ and H/sub /spl infin// filtering as well as a regional l/sub 2/ gain filtering feature developed specially for the saturation nonlinearity and is applicable to the digital transmultiplexer systems for the purpose of separating filterbank design. It turns out that our filter design can be carried out by solving a constrained optimization problem with linear matrix inequality (LMI) constraints. Simulations show that the resultant separating filters possess satisfactory reconstruction performance while working in the linear range and less degraded reconstruction performance in the presence of saturation.     

Robust Adaptive Fault Estimation for a Class of Nonlinear Systems Subject to Multiplicative Faults

In this paper, a fault estimation problem for a class of nonlinear systems subject to multiplicative faults and unknown disturbances is investigated. Multiplicative faults usually mixed with system states and inputs can cause additional complexity in the design of fault estimator due to parameter changes within process. Especially for the nonlinear system corrupted with unknown disturbances, it is not an easy work to distinguish the real fault factor from the mixed term. Under the nonlinear Lipschitz condition, the proposed robust adaptive fault estimation approach not only estimates the multiplicative faults and system states simultaneously, but also extracts the real effect of the faults. Meanwhile, the effect of disturbances is restricted to an L ₂ gain performance criteria which can be formulated into the basic feasibility problem of a linear matrix inequality (LMI). In order to reduce the conservatism of the proposed method, a relaxing Lipschitz matrix is introduced. Finally, an illustrative example is applied to verify the efficiency of the proposed robust adaptive estimation scheme.