Fault detection, isolation, and reconfiguration (FDIR) is an important and challenging problem in many engineering applications and continues to be an active area of research in the control community. This paper presents a survey of the various model-based FDIR methods developed in the last decade. In the paper, the FDIR problem is divided into the fault detection and isolation (FDI) step, and the controller reconfiguration step. For FDI, we discuss various model-based techniques to generate residuals that are robust to noise, unknown disturbance, and model uncertainties, as well as various statistical techniques of testing the residuals for abrupt changes (or faults). We then discuss various techniques of implementing reconfigurable control strategy in response to faults.

A Survey of Fault Detection, Isolation, and Reconfiguration Methods

The paper deals with the robust fault detection problem for Takagi-Sugeno (T-S) fuzzy Ito stochastic systems. Our aim is to develop a robust fault detection approach to the T-S fuzzy systems with Brownian motion. By using a general observer-based fault detection filter as a residual generator, the robust fault detection is formulated as a filtering problem. Attention is focused on the design of both the fuzzy-rule-independent and the fuzzy-rule-dependent fault detection filters guaranteeing a prescribed noise attenuation level in an Hinfin sense. Sufficient conditions are proposed to guarantee the mean-square asymptotic stability with an Hinfin performance for the fault detection system. The corresponding solvability conditions for the desired fuzzy-rule-independent and fuzzy-rule-dependent fault detection filters are also established. Finally, a numerical example is provided to illustrate the effectiveness of the proposed theory.

Fuzzy Filter Design for ItÔ Stochastic Systems With Application to Sensor Fault Detection

Markovian jump systems are a special class of hybrid and stochastic systems which can be used to describe many real world applications, such as manufacturing systems, power systems, chemical systems, economic systems, communication and control, etc. In this paper, a survey on recent developments of modeling, analysis and design of Markovian jump systems is presented. First, stability issues on Markovian jump systems are addressed. Then a variety of control and filter design methods are systematically recalled. Furthermore, the new trends of Markovian jump systems with uncertain transition rates as well as semi-Markovian jump systems are also discussed.

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A survey on Markovian jump systems: Modeling and design

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.

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Robust Fault Detection for Switched Linear Systems With State Delays

This paper is concerned with the network-based robust fault detection problem for a class of uncertain discrete-time Takagi-Sugeno fuzzy systems with stochastic mixed time delays and successive packet dropouts. The mixed time delays comprise both the multiple discrete time delays and the infinite distributed delays. A sequence of stochastic variables is introduced to govern the random occurrences of the discrete time delays, distributed time delays, and successive packet dropouts, where all the stochastic variables are mutually independent but obey the Bernoulli distribution. The main purpose of this paper is to design a fuzzy fault detection filter such that 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 fuzzy fault detection filters, and then, the corresponding solvability conditions for the desired filter gains are established. In addition, the optimal performance index for the addressed robust fuzzy fault detection problem is obtained by solving an auxiliary convex optimization problem. An illustrative example is provided to show the usefulness and effectiveness of the proposed design method.

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Fuzzy-Model-Based Robust Fault Detection With Stochastic Mixed Time Delays and Successive Packet Dropouts

The paper deals with the robust fault detection problem for a class of discrete-time linear Markovian jump systems with an unknown input. By using a general observer-based fault detection filter as residual generator, the robust fault detection filter design is formulated as an H∞-filtering problem, in which the filter matrices are dependent on the system mode, i.e. the residual generator is a Markovian jump linear system as well. The main objective is to make the error between residual and fault (or, more generally, weighted fault) as small as possible. A sufficient condition to solve this problem is established in terms of the feasibility of certain linear matrix inequalities (LMI), which can be solved with the aid of Matlab LMI Toolbox. A numerical example is given to illustrate the effectiveness of the proposed techniques.

G Wang

Papers

Fault detection for Markovian jump systems