There is an increasing demand for dynamic systems to become safer and more reliable This requirement extends beyond the normally accepted safety-critical systems such as nuclear reactors and aircraft, where safety is of paramount importance, to systems such as autonomous vehicles and process control systems where the system availability is vital It is clear that fault diagnosis is becoming an important subject in modern control theory and practice Robust Model-Based Fault Diagnosis for Dynamic Systems presents the subject of model-based fault diagnosis in a unified framework It contains many important topics and methods; however, total coverage and completeness is not the primary concern The book focuses on fundamental issues such as basic definitions, residual generation methods and the importance of robustness in model-based fault diagnosis approaches In this book, fault diagnosis concepts and methods are illustrated by either simple academic examples or practical applications The first two chapters are of tutorial value and provide a starting point for newcomers to this field The rest of the book presents the state of the art in model-based fault diagnosis by discussing many important robust approaches and their applications This will certainly appeal to experts in this field Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research The book is useful for both researchers in academia and professional engineers in industry because both theory and applications are discussed Although this is a research monograph, it will be an important text for postgraduate research students world-wide The largest market, however, will be academics, libraries and practicing engineers and scientists throughout the world

Robust Model-Based Fault Diagnosis for Dynamic Systems

On robust estimation of the location parameter

The Theory of Matrices. By F R. Gantmacher. Two volumes, pp. 374 and 276. 1959. (Translated from the Russian by K. A. Hirsch; Chelsea Publishing Company, New York)

https://users.encs.concordia.ca/~ymzhang/publications/ARC32-2-98Zhang_pp.229-252.pdf

Bibliographical review on reconfigurable fault-tolerant control systems

With the continuous increase in complexity and expense of industrial systems, there is less tolerance for performance degradation, productivity decrease, and safety hazards, which greatly necessitates to detect and identify any kinds of potential abnormalities and faults as early as possible and implement real-time fault-tolerant operation for minimizing performance degradation and avoiding dangerous situations. During the last four decades, fruitful results have been reported about fault diagnosis and fault-tolerant control methods and their applications in a variety of engineering systems. The three-part survey paper aims to give a comprehensive review of real-time fault diagnosis and fault-tolerant control, with particular attention on the results reported in the last decade. In this paper, fault diagnosis approaches and their applications are comprehensively reviewed from model- and signal-based perspectives, respectively.

/pdf/a-survey-of-fault-diagnosis-and-fault-tolerant-techniques-9uuf1uqysf.pdf

A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

In this paper, we have solved the problem of simultaneously estimating the state and input of linear time varying stochastic systems with uncertain noise covariance. The approach suggested rests on the use of the Proportional-Integral Two Stages Kalman Filter (PI-TSKF). This technique is qualified to be robust against the uncertainty. The filter is tested by an illustrative example.

Robust input and state estimation for linear discrete-time systems using PI two stages Kalman filter

Identification de systemes a commutation. Abdelfettah Hocine, Didier Maquin, Jose Ragot Centre de Recherche en Automatique de Nancy, CNRS UMR 7039 Institut National Polytechnique de Lorraine 2, Avenue de la foret de Haye, 54516 Vandœuvre-les-Nancy Cedex, FRANCE {Abdelfettah Hocine, Didier.Maquin, Jose.Ragot}@ensem.inpl-nancy.fr RESUME L’objectif de cette etude est l’estimation de l’etat d’un systeme a modes multiples connaissant ses entrees et ses sorties. La difficulte sous jacente est liee au fait que la matrice d’etat du systeme n’est pas unique mais prend ses valeurs dans un ensemble fini a valeurs non connues. Le probleme pose concerne donc la recon- naissance de la matrice d’etat active a un instant donne, l’estimation des parametres de cette matrice d’etat et l’estimation de l’etat du systeme. Se basant sur le principe utilise dans l’approche EM (expectation maximization) mais sans utiliser de variables dites cachees pour affecter les donnees aux modes de fonctionnement, on propose une estimation simultanee des parametres des modeles caracterisant les modes de fonctionnement et des instant

Identification de systèmes à commutation

Ce travail s'interesse a l'estimation d'etat de systemes lineaires a temps discret et en presence d'entrees inconnues en utilisant un observateur de type proportionnel multi-integral sous une forme generale (PI^p) L'introduction de multiples actions integrales dans l'observateur d'etat, permet d'assurer une bonne precision de l'estimation meme en presence des entrees inconnues decrites par des polynomes de degre p Un exemple d'application numerique montre la validite de cette approche en utilisant un observateur dote de trois actions integrales sur un systeme affecte par une entree inconnue polynomiale

https://hal.archives-ouvertes.fr/hal-00498198/document

Observateur proportionnel multi-intégral pour les systèmes linéaires à entrées inconnues

This paper presents a new recursive optimal filter structure for joint input and state estimation of linear time-varying discrete systems in the presence of unknown inputs. The method is concerned with the direct feedthrough matrix which has an arbitrary rank. The resulting filter is optimal in the sense of the unbiased minimum-variance (UMV) criteria. The paper extends the existing results, in particular the ones of Hsieh in 2009. A numerical example is given in order to make a comparison between the proposed filter and the Hsieh filter developed by [7].

Novel recursive optimal filter for the joint input-state estimation in linear discrete-time systems

Pour analyser le bon fonctionnement d'un systeme, l'une des techniques couramment utilisees consiste a tester la nullite d'equations de redondance entre differentes grandeurs connues caracterisant ce systeme. Ces equations sont generalement obtenues en eliminant, des equations modelisant le systeme, les grandeurs inconnues ; dans le cas ou les parametres de ce modele sont incertains, le resultat de l'elimination est critiquable. Dans cette communication, on expose ce probleme ainsi qu'une technique de generation d'equations de redondance robustes vis-a-vis des imprecisions des parametres. Des exemples illustrent les differentes propositions.

José Ragot

Papers

Robust input and state estimation for linear discrete-time systems using PI two stages Kalman filter

Identification de systèmes à commutation

Observateur proportionnel multi-intégral pour les systèmes linéaires à entrées inconnues

Novel recursive optimal filter for the joint input-state estimation in linear discrete-time systems

Espace de parité pour les systèmes linéaires incertains. Synthèse, quelques résultats nouveaux et mise en œuvre