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1
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Towards robustness in neural network based fault diagnosis

100%
Patan K., Witczak M., Korbicz J.
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
443-454
EN
Challenging design problems arise regularly in modern fault diagnosis systems. Unfortunately, classical analytical techniques often cannot provide acceptable solutions to such difficult tasks. This explains why soft computing techniques such as neural networks become more and more popular in industrial applications of fault diagnosis. Taking into account the two crucial aspects, i.e., the nonlinear behaviour of the system being diagnosed as well as the robustness of a fault diagnosis scheme with respect to modelling uncertainty, two different neural network based schemes are described and carefully discussed. The final part of the paper presents an illustrative example regarding the modelling and fault diagnosis of a DC motor, which shows the performance of the proposed strategy.
2
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Robust nonlinear observer design for actuator fault detection in diesel engines

100%
Boulkroune B., Djemili I., Aitouche A., Cocquempot V.
International Journal of Applied Mathematics and Computer Science
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2013
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tom 23
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nr 3
557-569
EN
This paper is concerned with actuator fault detection in nonlinear systems in the presence of disturbances. A nonlinear unknown input observer is designed and the output estimation error is used as a residual for fault detection. To deal with the problem of high Lipschitz constants, a modified mean-value theorem is used to express the nonlinear error dynamics as a convex combination of known matrices with time-varying coefficients. Moreover, the disturbance attenuation is performed using a modified $H_∞$ criterion. A sufficient condition for the existence of an unknown input observer is obtained using a linear matrix inequality formula, and the observer gains are obtained by solving the corresponding set of inequalities. The advantages of the proposed method are that no a priori assumption on the unknown input is required and that it can be applied to a large class of nonlinear systems. Performances of the proposed approach are shown through the application to a diesel engine model.
3
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A method for sensor placement taking into account diagnosability criteria

100%
Yassine A., Ploix S., Flaus J.-M.
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
497-512
EN
This paper presents a new approach to sensor placement based on diagnosability criteria. It is based on the study of structural matrices. Properties of structural matrices regarding detectability, discriminability and diagnosability are established in order to be used by sensor placement methods. The proposed approach manages any number of constraints modelled by linear or nonlinear equations and it does not require the design of analytical redundancy relations. Assuming that a constraint models a component and that the cost of the measurement of each variable is defined, a method determining sensor placements satisfying diagnosability specifications, where all the diagnosable, discriminable and detectable constraint sets are specified, is proposed. An application example dealing with a dynamical linear system is presented.
4
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Advances in model-based fault diagnosis with evolutionary algorithms and neural networks

100%
Witczak M.
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
85-99
EN
Challenging design problems arise regularly in modern fault diagnosis systems. Unfortunately, the classical analytical techniques often cannot provide acceptable solutions to such difficult tasks. This explains why soft computing techniques such as evolutionary algorithms and neural networks become more and more popular in industrial applications of fault diagnosis. The main objective of this paper is to present recent developments regarding the application of evolutionary algorithms and neural networks to fault diagnosis. In particular, a brief introduction to these computational intelligence paradigms is presented, and then a review of their fault detection and isolation applications is performed. Close attention is paid to techniques that integrate the classical and soft computing methods. A selected group of them is carefully described in the paper. The performance of the presented approaches is illustrated with the use of the DAMADICS fault detection benchmark that deals with a valve actuator.
5
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Fault diagnosis of a water for injection system using enhanced structural isolation

100%
Laursen M., Blanke M., Düştegör 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
593-603
EN
A water for injection system supplies chilled sterile water as a solvent for pharmaceutical products. There are ultimate requirements for the quality of the sterile water, and the consequence of a fault in temperature or in flow control within the process may cause a loss of one or more batches of the production. Early diagnosis of faults is hence of considerable interest for this process. This study investigates the properties of multiple matchings with respect to isolability, and it suggests to explore the topologies of multiple use-modes for the process and to employ active techniques for fault isolation to enhance structural isolability of faults. The suggested methods are validated on a high-fidelity simulation of the process.
6
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A model-based approach to fault-tolerant control

88%
Niemann H.
International Journal of Applied Mathematics and Computer Science
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2012
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tom 22
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nr 1
67-86
EN
A model-based controller architecture for Fault-Tolerant Control (FTC) is presented in this paper. The controller architecture is based on a general controller parameterization. The FTC architecture consists of two main parts, a Fault Detection and Isolation (FDI) part and a controller reconfiguration part. The theoretical basis for the architecture is given followed by an investigation of the single parts in the architecture. It is shown that the general controller parameterization is central in connection with both fault diagnosis as well as controller reconfiguration. Especially in relation to the controller reconfiguration part, the application of controller parameterization results in a systematic technique for switching between different controllers. This also allows controller switching using different sets of actuators and sensors.
7
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Bayesian reliability models of Weibull systems: State of the art

88%
Zaidi A., Ould Bouamama B., Tagina M.
International Journal of Applied Mathematics and Computer Science
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2012
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tom 22
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nr 3
585-600
EN
In the reliability modeling field, we sometimes encounter systems with uncertain structures, and the use of fault trees and reliability diagrams is not possible. To overcome this problem, Bayesian approaches offer a considerable efficiency in this context. This paper introduces recent contributions in the field of reliability modeling with the Bayesian network approach. Bayesian reliability models are applied to systems with Weibull distribution of failure. To achieve the formulation of the reliability model, Bayesian estimation of Weibull parameters and the model's goodness-of-fit are evoked. The advantages of this modelling approach are presented in the case of systems with an unknown reliability structure, those with a common cause of failures and redundant ones. Finally, we raise the issue of the use of BNs in the fault diagnosis area.
8
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Fault isolability with different forms of the faults-symptoms relation

88%
Kościelny J., Syfert M., Rostek K., Sztyber A.
International Journal of Applied Mathematics and Computer Science
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2016
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tom 26
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nr 4
815-826
EN
The definitions and conditions for fault isolability of single faults for various forms of the diagnostic relation are reviewed. Fault isolability and unisolability on the basis of a binary diagnostic matrix are analyzed. Definitions for conditional and unconditional isolability and unisolability on the basis of a fault information system (FIS), symptom sequences and directional residuals are formulated. General definitions for conditional and unconditional isolability and unisolability in the cases of simultaneous evaluation of diagnostic signal values and a sequence of symptoms are provided. A comprehensive example is discussed.
9
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A new efficient and flexible algorithm for the design of testable subsystems

88%
Ploix S., Yassine A., Flaus J.-M.
International Journal of Applied Mathematics and Computer Science
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2010
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tom 20
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nr 1
175-190
EN
In complex industrial plants, there are usually many sensors and the modeling of plants leads to lots of mathematical relations. This paper presents a general method for finding all the possible testable subsystems, i.e., sets of relations that can lead to various types of detection tests. This method, which is based on structural analysis, provides the constraints that have to be used for the design of each detection test and manages situations where constraints contain non-deductible variables and where some constraints cannot be gathered in the same test. Thanks to these results, it becomes possible to select the most interesting testable subsystems regarding detectability and diagnosability criteria. Application examples dealing with a road network, a digital counter and an electronic circuit are presented.
10
Artykuł dostępny w postaci pełnego tekstu - kliknij by otworzyć plik
Content available

Robust sensor fault estimation for descriptor-LPV systems with unmeasurable gain scheduling functions: application to an anaerobic bioreactor

88%
López-Estrada F.-R., Ponsart J.-C., Theilliol D., Astorga-Zaragoza C.-M., Camas-Anzueto J.-L.
International Journal of Applied Mathematics and Computer Science
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2015
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tom 25
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nr 2
233-244
EN
This paper addresses the design of a state estimation and sensor fault detection, isolation and fault estimation observer for descriptor-linear parameter varying (D-LPV) systems. In contrast to where the scheduling functions depend on some measurable time varying state, the proposed method considers the scheduling function depending on an unmeasurable state vector. In order to isolate, detect and estimate sensor faults, an augmented system is constructed by considering faults to be auxiliary state vectors. An unknown input LPV observer is designed to estimate simultaneously system states and faults. Sufficient conditions to guarantee stability and robustness against the uncertainty provided by the unmeasurable scheduling functions and the influence of disturbances are synthesized via a linear matrix inequality (LMI) formulation by considering H∞ and Lyapunov approaches. The performances of the proposed method are illustrated through the application to an anaerobic bioreactor model.
11
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Content available

A Hamiltonian approach to fault isolation in a planar vertical take-off and landing aircraft model

88%
Rodriguez-Alfaro L., Alcorta-Garcia E., Lara D., Romero 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
65-76
EN
The problem of fault detection and isolation in a class of nonlinear systems having a Hamiltonian representation is considered. In particular, a model of a planar vertical take-off and landing aircraft with sensor and actuator faults is studied. A Hamiltonian representation is derived from an Euler-Lagrange representation of the system model considered. In this form, nonlinear decoupling is applied in order to obtain subsystems with (as much as possible) specific fault sensitivity properties. The resulting decoupled subsystem is represented as a Hamiltonian system and observer-based residual generators are designed. The results are presented through simulations to show the effectiveness of the proposed approach.
12
Content available remote

Fault monitoring and fault recovery control for position-moored vessels

88%
Fang S., Blanke M.
International Journal of Applied Mathematics and Computer Science
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2011
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tom 21
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nr 3
467-478
EN
This paper addresses fault-tolerant control for position mooring of a shuttle or floating production storage and offloading vessels. A complete framework for fault diagnosis is presented. A loss of a sub-sea mooring line buoyancy element and line breakage are given particular attention, since such failures might cause high-risk abortion of an oil-loading operation. With significant drift forces from waves, non-Gaussian elements dominate forces and the residuals designed for fault diagnosis. Hypothesis testing is designed using dedicated change detection for the type of distribution encountered. A new position recovery algorithm is proposed as a means of fault accommodation in order to keep the mooring system in a safe state, despite faults. The position control is shown to be capable of accommodating serious failures and preventing breakage of a mooring line, or a loss of a buoyancy element, from causing subsequent failures. Properties of the detection and fault-tolerant control algorithms are demonstrated by high fidelity simulations.
13
Content available remote

Signed directed graph based modeling and its validation from process knowledge and process data

88%
Yang F., Shah S., Xiao D.
International Journal of Applied Mathematics and Computer Science
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2012
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tom 22
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nr 1
41-53
EN
This paper is concerned with the fusion of information from process data and process connectivity and its subsequent use in fault diagnosis and process hazard assessment. The Signed Directed Graph (SDG), as a graphical model for capturing process topology and connectivity to show the causal relationships between process variables by material and information paths, has been widely used in root cause and hazard propagation analysis. An SDG is usually built based on process knowledge as described by piping and instrumentation diagrams. This is a complex and experience-dependent task, and therefore the resulting SDG should be validated by process data before being used for analysis. This paper introduces two validation methods. One is based on cross-correlation analysis of process data with assumed time delays, while the other is based on transfer entropy, where the correlation coefficient between two variables or the information transfer from one variable to another can be computed to validate the corresponding paths in SDGs. In addition to this, the relationship captured by data-based methods should also be validated by process knowledge to confirm its causality. This knowledge can be realized by checking the reachability or the influence of one variable on another based on the corresponding SDG which is the basis of causality. A case study of an industrial process is presented to illustrate the application of the proposed methods.
14
Content available remote

Fault tolerance in networked control systems under intermittent observations

88%
Georges J.-P., Theilliol D., Cocquempot V., Ponsart J.-C., Aubrun C.
International Journal of Applied Mathematics and Computer Science
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2011
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tom 21
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nr 4
639-648
EN
This paper presents an approach to fault tolerant control based on the sensor masking principle in the case of wireless networked control systems. With wireless transmission, packet losses act as sensor faults. In the presence of such faults, the faulty measurements corrupt directly the behaviour of closed-loop systems. Since the controller aims at cancelling the error between the measurement and its reference input, the real outputs will, in such a networked control system, deviate from the desired value and may drive the system to its physical limitations or even to instability. The proposed method facilitates fault compensation based on an interacting multiple model approach developed in the framework of channel errors or network congestion equivalent to multiple sensors failures. The interacting multiple model method involved in a networked control system provides simultaneously detection and isolation of on-line packet losses, and also performs a suitable state estimation. Based on particular knowledge of packet losses, sensor fault-tolerant controls are obtained by computing a new control law using fault-free estimation of the faulty element to avoid intermittent observations that might develop into failures and to minimize the effects on system performance and safety.
15
Content available remote

Fault diagnosis of networked control systems

75%
Aubrun C., Sauter D., Yamé J.
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
525-537
EN
Networked Control Systems (NCSs) deal with feedback control systems with loops closed via data communication networks. Control over a network has many advantages compared with traditionally controlled systems, such as a lower implementation cost, reduced wiring, simpler installation and maintenance and higher reliability. Nevertheless, the networkinduced delay, packet dropout, asynchronous behavior and other specificities of networks will degrade the performance of closed-loop systems. In this context, it is necessary to develop a new theory for systems that operate in a distributed and asynchronous environment. Research on Fault Detection and Isolation (FDI) for NCSs has received increasing attention in recent years. This paper reviews the state of the art in this topic.
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