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1
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Supervisory fault tolerant control with integrated fault detection and isolation: A switched system approach

100%
Yang H., Jiang B., Cocquempot V., Lu L.
International Journal of Applied Mathematics and Computer Science
|
2012
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tom 22
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nr 1
87-97
EN
This paper focuses on supervisory fault tolerant control design for a class of systems with faults ranging over a finite cover. The proposed framework is based on a switched system approach, and relies on a supervisory switching within a family of pre-computed candidate controllers without individual fault detection and isolation schemes. Each fault set can be accommodated either by one candidate controller or by a set of controllers under an appropriate switching law. Two aircraft examples are included to illustrate the efficiency of the proposed method.
2
Content available remote

Input reconstruction by means of system inversion: A geometric approach to fault detection and isolation in nonlinear systems

88%
Edelmayer A., Bokor J., Szabó Z., Szigeti F.
International Journal of Applied Mathematics and Computer Science
|
2004
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tom 14
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nr 2
189-199
EN
In this paper the classical detection filter design problem is considered as an input reconstruction problem. Input reconstruction is viewed as a dynamic inversion problem. This approach is based on the existence of the left inverse and arrives at detector architectures whose outputs are the fault signals while the inputs are the measured system inputs and outputs and possibly their time derivatives. The paper gives a brief summary of the properties and existence of the inverse for linear and nonlinear multivariable systems. A view of the inversion-based input reconstruction with special emphasis on the aspects of fault detection and isolation by using invariant subspaces and the results of classical geometrical systems theory is provided. The applicability of the idea to fault reconstruction is demonstrated through examples.
3
Content available remote

Active fault tolerant control of nonlinear systems: The cart-pole example

88%
Bonfè M., Castaldi P., Mimmo N., Simani S.
International Journal of Applied Mathematics and Computer Science
|
2011
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tom 21
|
nr 3
441-455
EN
This paper describes the design of fault diagnosis and active fault tolerant control schemes that can be developed for nonlinear systems. The methodology is based on a fault detection and diagnosis procedure relying on adaptive filters designed via the nonlinear geometric approach, which allows obtaining the disturbance de-coupling property. The controller reconfiguration exploits directly the on-line estimate of the fault signal. The classical model of an inverted pendulum on a cart is considered as an application example, in order to highlight the complete design procedure, including the mathematical aspects of the nonlinear disturbance de-coupling method based on the nonlinear differential geometry, as well as the feasibility and efficiency of the proposed approach. Extensive simulations of the benchmark process and Monte Carlo analysis are practical tools for assessing experimentally the robustness and stability properties of the developed fault tolerant control scheme, in the presence of modelling and measurement errors. The fault tolerant control method is also compared with a different approach relying on sliding mode control, in order to evaluate benefits and drawbacks of both techniques. This comparison highlights that the proposed design methodology can constitute a reliable and robust approach for application to real nonlinear processes.
4
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Residual generator fuzzy identification for automotive diesel engine fault diagnosis

88%
Simani S.
International Journal of Applied Mathematics and Computer Science
|
2013
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tom 23
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nr 2
419-438
EN
Safety in dynamic processes is a concern of rising importance, especially if people would be endangered by serious system failure. Moreover, as the control devices which are now exploited to improve the overall performance of processes include both sophisticated control strategies and complex hardware (input-output sensors, actuators, components and processing units), there is an increased probability of faults. As a direct consequence of this, automatic supervision systems should be taken into account to diagnose malfunctions as early as possible. One of the most promising methods for solving this problem relies on the analytical redundancy approach, in which residual signals are generated. If a fault occurs, these residual signals are used to diagnose the malfunction. This paper is focused on fuzzy identification oriented to the design of a bank of fuzzy estimators for fault detection and isolation. The problem is treated in its different aspects covering the model structure, the parameter identification method, the residual generation technique, and the fault diagnosis strategy. The case study of a real diesel engine is considered in order to demonstrate the effectiveness the proposed methodology.
5
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A double window state observer for detection and isolation of abrupt changes in parameters

88%
Byrski J., Byrski W.
International Journal of Applied Mathematics and Computer Science
|
2016
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tom 26
|
nr 3
585-602
EN
The paper presents a new method for diagnosis of a process fault which takes the form of an abrupt change in some real parameter of a time-continuous linear system. The abrupt fault in the process real parameter is reflected in step changes in many parameters of the input/output model as well as in step changes in canonical state variables of the system. Detection of these state changes will enable localization of the faulty parameter in the system. For detecting state changes, a special type of exact state observer will be used. The canonical state will be represented by the derivatives of the measured output signal. Hence the exact state observer will play the role of virtual sensors for reconstruction of the derivatives of the output signal. For designing the exact state observer, the model parameters before and after the moment of fault occurrence must be known. To this end, a special identification method with modulating functions will be used. A novel concept presented in this paper concerns the structure of the observer. It will take the form of a double moving window observer which consists of two signal processing windows, each of width T . These windows are coupled to each other with a common edge. The right-hand side edge of the left-side moving window in the interval [t - 2T, t - T ] is connected to the left-hand side edge of the right-side window which operates in the interval [t - T, t]. The double observer uses different measurements of input/output signals in both the windows, and for each current time t simultaneously reconstructs two values of the state- the final value of the state in the left-side window zT (t - T ) and the initial value of the state z0 (t - T ) in the right-side window. If the process parameters are constant, the values of both the states on the common joint edge are the same. If an abrupt change (fault) in some parameter at the moment tA = t - T occurs in the system, then step changes in some variables of the canonical state vector will also occur and the difference between the states will be detected. This will enable localization of the faulty parameter in the system.
6
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Nonlinear diagnostic filter design: algebraic and geometric points of view

75%
Shumsky A., Zhirabok A.
International Journal of Applied Mathematics and Computer Science
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2006
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tom 16
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nr 1
115-127
EN
The problem of diagnostic filter design is studied. Algebraic and geometric approaches to solving this problem are investigated. Some relations between these approaches are established. New definitions of fault detectability and isolability are formulated. On the basis of these definitions, a procedure for diagnostic filter design is given in both algebraic and geometric terms.
7
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Fault detection and isolation with robust principal component analysis

75%
Tharrault Y., Mourot G., Ragot J., Maquin D.
International Journal of Applied Mathematics and Computer Science
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2008
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tom 18
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nr 4
429-442
EN
Principal component analysis (PCA) is a powerful fault detection and isolation method. However, the classical PCA, which is based on the estimation of the sample mean and covariance matrix of the data, is very sensitive to outliers in the training data set. Usually robust principal component analysis is applied to remove the effect of outliers on the PCA model. In this paper, a fast two-step algorithm is proposed. First, the objective was to find an accurate estimate of the covariance matrix of the data so that a PCA model might be developed that could then be used for fault detection and isolation. A very simple estimate derived from a one-step weighted variance-covariance estimate is used (Ruiz-Gazen, 1996). This is a “local” matrix of variance which tends to emphasize the contribution of close observations in comparison with distant observations (outliers). Second, structured residuals are used for multiple fault detection and isolation. These structured residuals are based on the reconstruction principle, and the existence condition of such residuals is used to determine the detectable faults and the isolable faults. The proposed scheme avoids the combinatorial explosion of faulty scenarios related to multiple faults to be considered. Then, this procedure for outliers detection and isolation is successfully applied to an example with multiple faults.
8
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SMAC-FDI: A single model active fault detection and isolation system for unmanned aircraft

75%
Ducard G.
International Journal of Applied Mathematics and Computer Science
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2015
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tom 25
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nr 1
189-201
EN
This article presents a single model active fault detection and isolation system (SMAC-FDI) which is designed to efficiently detect and isolate a faulty actuator in a system, such as a small (unmanned) aircraft. This FDI system is based on a single and simple aerodynamic model of an aircraft in order to generate some residuals, as soon as an actuator fault occurs. These residuals are used to trigger an active strategy based on artificial exciting signals that searches within the residuals for the signature of an actuator fault. Fault isolation is carried out through an innovative mechanism that does not use the previous residuals but the actuator control signals directly. In addition, the paper presents a complete parameter-tuning strategy for this FDI system. The novel concepts are backed-up by simulations of a small unmanned aircraft experiencing successive actuator failures. The robustness of the SMAC-FDI method is tested in the presence of model uncertainties, realistic sensor noise and wind gusts. Finally, the paper concludes with a discussion on the computational efficiency of the method and its ability to run on small microcontrollers.
9
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Content available

Switching time estimation and active mode recognition using a data projection method

75%
Hakem A., Cocquempot V., Pekpe K.
International Journal of Applied Mathematics and Computer Science
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2016
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tom 26
|
nr 4
827-840
EN
This paper proposes a data projection method (DPM) to detect a mode switching and recognize the current mode in a switching system. The main feature of this method is that the precise knowledge of the system model, i.e., the parameter values, is not needed. One direct application of this technique is fault detection and identification (FDI) when a fault produces a change in the system dynamics. Mode detection and recognition correspond to fault detection and identification, and switching time estimation to fault occurrence time estimation. The general principle of the DPM is to generate mode indicators, namely, residuals, using matrix projection techniques, where matrices are composed of input and output measured data. The DPM is presented in detail, and properties of switching detectability (fault detectability) and discernability between modes (fault identifiability) are characterized and discussed. The great advantage of this method, compared with other techniques in the literature, is that it does not need the model parameter values and thus can be applied to systems of the same type without identifying their parameters. This is particularly interesting in the design of generic embedded fault diagnosis algorithms.
10
Content available remote

Redundancy relations for fault diagnosis in nonlinear uncertain systems

75%
Shumsky A.
International Journal of Applied Mathematics and Computer Science
|
2007
|
tom 17
|
nr 4
477-489
EN
The problem of fault detection and isolation in nonlinear uncertain systems is studied within the scope of the analytical redundancy concept. The problem solution involves checking the redundancy relations existing among measured system inputs and outputs. A novel method is proposed for constructing redundancy relations based on system models described by differential equations whose right-hand sides are polynomials. The method involves a nonlinear transformation of the initial system model into a strict feedback form. Algebraic and geometric tools are used for this transformation. The features of the method are made particular for uncertain systems with a linear structure.
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