The research in this thesis is undertaken by observing that modern systems are becoming more and more complex and safety-critical due to the increasing requirements on system smartness and autonomy, and as a result health monitoring system needs to be developed to meet the requirements on system safety and reliability. The state-of-the-art approaches to monitoring system status are model based Fault Diagnosis (FD) systems, which can fuse the advantages of system physical modelling and sensors' characteristics. A number of model based FD approaches have been proposed. The conventional residual based approaches by monitoring system output estimation errors, however, may have certain limitations such as complex diagnosis logic for fault isolation, less sensitiveness to system faults and high computation load. More importantly, little attention has been paid to the problem of fault diagnosis system verification which answers the question that under what condition (i.e., level of uncertainties) a fault diagnosis system is valid. To this end, this thesis investigates the design and verification of fault diagnosis algorithms. It first highlights the differences between two popular FD approaches (i.e., residual based and fault estimation based) through a case study. On this basis, a set of uncertainty estimation algorithms are proposed to generate fault estimates according to different specifications after interpreting the FD problem as an uncertainty estimation problem. Then FD algorithm verification and threshold selection are investigated considering that there are always some mismatches between the real plant and the mathematical model used for FD observer design. Reachability analysis is drawn to evaluate the effect of uncertainties and faults such that it can be quantitatively verified under what condition a FD algorithm is valid. First the proposed fault estimation algorithms in this thesis, on the one hand, extend the existing approaches by pooling the available prior information such that performance can be enhanced, and on the other hand relax the existence condition and reduce the computation load by exploiting the reduced order observer structure. Second, the proposed framework for fault diagnosis system verification bridges the gap between academia and industry since on the one hand a given FD algorithm can be verified under what condition it is effective, and on the other hand different FD algorithms can be compared and selected for different application scenarios. It should be highlighted that although the algorithm design and verification are for fault diagnosis systems, they can also be applied for other systems such as disturbance rejection control system among many others.

Made available in DSpace on 2014-12-17T14:55:03Z (GMT). No. of bitstreams: 1 RaphaelaGF.pdf: 1672960 bytes, checksum: 5b6b120f4026f9849183e5f96e363672 (MD5) Previous issue date: 2007-02-08 / ?This master dissertation presents the development of a fault detection and isolation system based in neural network. The system is composed of two parts: an identification subsystem and a classification subsystem. Both of the subsystems use neural network techniques with multilayer perceptron training algorithm. Two approaches for identifica-tion stage were analyzed. The fault classifier uses only residue signals from the identification subsystem. To validate the proposal we have done simulation and real experiments in a level system with two water reservoirs. Several faults were generated above this plant and the proposed fault detection system presented very acceptable behavior. In the end of this work we highlight the main difficulties found in real tests that do not exist when it works only with simulation environments / ?Esta disserta??o de mestrado apresenta o desenvolvimento de um sistema de detec??o e isolamento de falhas (DIF) baseado em redes neurais. O sistema ? dividido em duas etapas: uma de identifica??o neural do sistema e outra de detec??o e classifica??o de falhas. Ambos subsistemas usam t?cnicas de redes neurais com o algoritmoBackpropa- gation para redes Perceptronde M?ltiplas Camadas. Duas abordagens para identifica??o neural foram testadas e uma delas selecionada para fazer parte do sistema DIF. Oclassifi-cador de falhas utiliza apenas valores residuais para a classifica??o das mesmas. Todos os testes foram realizados tanto em ambiente simulado quanto em ambiente real, no intuito de comprovar dificuldades encontradas em testes reais n?o existentes quando se trabalha apenas com simula??es

Les convertisseurs DC-DC suscitent un intérêt considérable en raison de leur puissance élevée et de leurs bonnes performances. Ils sont particulièrement utiles dans les systèmes multisources de production d'énergie électrique. Toutefois, en raison du grand nombre de composants sensibles utilisés dans ces circuits et comprenant des semi-conducteurs de puissance, des bobines et des condensateurs, une probabilité non négligeable de défaillance des composants doit être prise en compte. Cette thèse considère l'un des convertisseurs DC-DC les plus prometteurs - le convertisseur ZVS à pont isolé de type Buck. Une approche en deux étapes est présentée pour détecter et isoler les défauts en circuit ouvert dans les semi-conducteurs de puissance des convertisseurs DC-DC. La première étape concerne la détection et la localisation des défauts dans un convertisseur donne. La seconde étape concerne sur les systèmes munis de plusieurs convertisseurs DC-DC. Les méthodes proposées sont basées sur les réseaux Bayesiens (BBN). Les signaux utilisés dans ces méthodes sont ceux des entrées de mesure du système de commande et aucune mesure supplémentaire n'est requise. Un convertisseur expérimental ZVS à pont isolé de type Buck a été conçu et construit pour valider la détection et la localisation des défauts Sur un seul convertisseur. Ces méthodes peuvent être étendues à d'autres types de convertisseurs DC-DC. / DC-DC converters have received significant interest recently as a result of their high power capabilities and good power quality. They are of particular interest in systems with multiple sources of energy. However due to the large number of sensitive components including power semiconductor devices, coils, and capacitors used in such circuits there is a high likelihood of component failure. This thesis considers one of the most promising DC-DC converters—the ZVS full bridge isolated Buck converter. An approach with two stages is presented to detect and isolate opencircuit faults in the power semiconductor devices in systems with DC-DC converters. The first stage is the fault detection and isolation for a single DC-DC converter, while the second stage works on a system with multiple DC-DC converters. The proposed methods are based on Bayesian Belief Network (BBN). The signals used in the proposed methods are already available as measurement inputs to control system and no additional measurements are required. An experimental ZVS full bridge isolated Buck converter has been designed and built to validate the fault detection and isolation method on a single converter. The methods can be used with other DC-DC converter typologies employing similar analysis and principals.

Sensors are highly essential components in most modern control systems and are used in increasingly complex ways to improve system precision and reliability. Since they are generally susceptible to faults it is common to perform on-line fault diagnostics on sensor data to verify nominal behavior. This is especially important for safety critical systems where it can be imperative to identify, and react to, a fault before it increases in severity. An example of such a safety critical system is the propulsion control of a vehicle. In this thesis, three different model-based methods for Fault Detection and Isolation (FDI) are developed and tested with the aim of detecting and isolating sensor faults in the powertrain of an electric, center articulated, four-wheel-drive vehicle. First, kinematic models are derived that combine sensor data from all sensors related to propulsion. Second, the kinematic models are implemented in system observers to produce fault sensitive zero-mean residuals. Finally, fault isolation algorithms are derived, which detect and indicate different types of faults via evaluation of the observer residuals. The results show that all FDI methods can detect and isolate stochastic faults with high certainty, but that offset-type faults are hard to distinguish from modeling errors and are therefore easily attenuated by the system observers. Faults in accelerometer sensors need extra measures to be detectable, owing to the environment where the vehicle is typically operated. A nonlinear system model shows good conformity to the vehicle system, lending confidence to its further use as a driver for propulsion control.

Ce travail de thèse aborde le problème de la détection et de l’isolation des pannes à base de modèle du système dynamique non linéaire. Les techniques de détection et d’identification de pannes sont déjà appliquées aux systèmes industriels et elles jouent un rôle important pour assurer les performances attendues des systèmes automatiques. Les différentes approches du diagnostic des systèmes dynamiques semblent être souvent le résultat de contextes différents notamment en ce qui concerne les applications visées et le cahier des charges qui en résulte. Ainsi, la nature des informations disponibles sur le système ou le type de défauts à détecter conduit à la mise en œuvre de stratégies spécifiques. Dans cette étude on suppose disposer d’un modèle de fonctionnement du système et les pannes considérées sont celles qui conduisent le système à ne plus suivre ce modèle. Après avoir introduit la notion de platitude différentielle pour un système dynamique non linéaire continu, plusieurs exemples de systèmes dynamiques différentiellement plats sont introduits. Les redondances analytiques mises en évidence par cette propriété sont dans une première étape utilisées pour détecter des pannes. Ceci conduit à développer des estimateurs d’ordre supérieurs pour les dérivées des sorties plates du système et des estimateurs non linéaires de l’état du système. Cette approche est mise en œuvre dans le cadre de la détection de pannes des moteurs d’un Quadri-Rotor.La notion de platitude pour les systèmes dynamiques discrets est alors introduite. Il est alors possible de développer une nouvelle approche pour la détection des pannes, fondée sur la redondance temporelle entre les informations résultant des mesures directes de composantes du vecteur d’état du Quadri-Rotor et les estimations des sorties plates à chaque instant d’échantillonnage. Cette approche qui est illustrée ici aussi dans le cas du Quadri-Rotor, permet aussi de développer une méthode d’identification en ligne des pannes en se basant sur la chronologie de la propagation de leurs effets / This PhD is submitted in model-based faults detection and isolation in nonlinear dynamic system. The techniques of faults detection and isolation are already being applied to industrial systems and have played an important role to ensure the expected performance of automated systems. The differences in approaches to diagnosis of dynamic systems often seem to be the result of different contexts including in respect of applications and referred the specification that follows. Thus, the nature of information available on the system or the type of fault detection leads to the implementation of specific strategies. In this study we have assumed a model of system operation and faults considered are those that lead the system to no longer follow this model.After introducing the concept of differential flatness for a nonlinear dynamical system continued, several examples of differentially flat systems dynamics are introduced. The analytical redundancy highlighted by this property is a first step used to detect faults. This leads to develop estimators for higher order derivatives of the outputs flat of the system and estimator plate for nonlinear system state. This approach is implemented in the context of fault detection engine of a Quadri-Rotor.The notion of flatness for discrete dynamical systems is introduced. It is then possible to develop a new approach for fault detection based on temporal redundancy between the information from direct measurements of components of the state vector of Quadri-Rotor and estimates of output flat at each sampling instant. This approach is illustrated here as in the case of the Quadri-Rotor, can also develop a method for online identification of fault based on the chronology of the spread of their effects

<p>Cyber-physical systems (CPS) are a class of complicated systems integrating cyber components with physical components. Although such a cyber-physical interaction improves the system performance and intelligence, it increases the system complexity and makes the system vulnerable to various types of faults, failures, and cyber-attacks. To assure the security and improve the resiliency of CPS, it is found that the hybrid system model can be a powerful tool in the domain of fault detection and isolation, cyber-attack diagnosis and containment, as well as resilient control and reconfiguration. Several problems are concerned in this dissertation. For situational awareness, \textit{mode discernibility}, which stands for whether the discrete state of a hybrid system can be correctly identified, is characterized and discussed with potential applications to monitoring system design. For CPS vulnerability analysis, the problem of stealthy attack design for systems with switching structures is investigated, which is motivated by the recent literature. To further understand and remedy for the vulnerabilities, the detectability and identifiability for severe cyber-attacks are defined and characterized, which are followed by the discussions on the methodologies for cyber-attack detection and identification. Last but not least, based on the understanding of identifiability, a framework of resilient control design is proposed to mitigate the impact of cyber-attacks, which can be generalized in future to account for additional design criteria.</p>

No description available.

<p>This thesis considers the problem of fault diagnosis and fault-tolerant control (FTC) for chemical process systems with nonlinear dynamics. The primary objective of fault diagnosis discussed in this work is to identify the failed actuator or sensor by using the information embodied in a process model, as well as input and output data. To this end, an active fault isolation method is first proposed to identify actuator faults and process disturbances by utilizing control action and process nonlinearity. The key idea is to move the process to a region upon fault detection where the effect of each fault can be differentiated from others. The proposed method enables isolation of faults that may not be achievable under nominal operation. This work then investigates the problem of sensor fault isolation by exploiting model-based sensor redundancy through state observer design. Specifically, a high-gain observer is presented and the stability property of the closed-loop system is rigorously established. A method that uses a bank of high-gain observers is then proposed to isolate sensor faults, which explicitly accounts for process nonlinearity, and to continue nominal operation upon fault isolation. In addition to fault diagnosis, this work addresses the problem of handling severe actuator faults using a safe-parking approach and integrating fault diagnosis and safe-parking techniques in a unified fault-handling framework. In particular, several practical issues are considered for the design and implementation of safe-parking techniques, including changes in process dynamics, the network structure of a chemical plant, and actuators frozen at arbitrary positions. The advantage of this approach is that it enables stable process operation under faulty conditions, avoiding the partial or entire shutdown of a chemical plant and resulting economic losses. The efficacy of the proposed fault diagnosis and FTC methods is demonstrated through numerous simulations of chemical process examples.</p> / Doctor of Philosophy (PhD)

This work is intended to investigate the Aircraft Technical Diagnostics Systems by focusing on Central Maintenance Systems and the Fault Detection and Isolation (FDI) process among the avionic components. A review about the Computer Control Systems and background about the Avionic Architecture is presented prior to introducing to most popular FDI method; model-based diagnosis. The discussions about the onboard FDI practices are followed by a maintenance hangar FDI perspective which was concluded as a result of the field research. The outcomes of the field research and pointing the “real” fault isolation are analyzed to point the practical needs of a hangar FDI tools. Subsequently, a proposal technique, which can improve fault isolation by preventing No-Fault-Found (NFF), is given by discussing the methods to implement it. Finally, the results of investigation and conclusions of analysis are presented. / Baigiamajame darbe lietuviškos anotacijos nepateikta.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-08-28T16:50:59Z No. of bitstreams: 1 WAGNER DE SOUZA PORTO - DISSERTAÇÃO PPGCC 2009..pdf: 3676431 bytes, checksum: 91bede14a64447aa4598ba5c6b3365a4 (MD5) / Made available in DSpace on 2018-08-28T16:50:59Z (GMT). No. of bitstreams: 1 WAGNER DE SOUZA PORTO - DISSERTAÇÃO PPGCC 2009..pdf: 3676431 bytes, checksum: 91bede14a64447aa4598ba5c6b3365a4 (MD5) Previous issue date: 2009-09-16 / Este trabalho apresenta o Auto-FDI (Automatic Fault Detection and Isolation), uma ferramenta de detecção e isolamento de falhas em sistemas. A ferramenta usa o conceito de redundância analítica, onde sinais obtidos do sistema (possivelmente com falha) são comparados com sinais esperados, obtidos de um modelo. O isolamento de falhas emprega uma técnica desenvolvida neste trabalho, chamada isolamento automático. A técnica usa uma abordagem baseada em grafos que considera a propagação de falhas e a falta de informação sobre determinados componentes do sistema. Falhas são localizadas de forma mais precisa possível, dado o nível de detalhe do modelo. No escopo deste trabalho foi abordado todo o processo de especificação, projeto, implementação e validação da ferramenta, utilizada como prova de conceito para a técnica desenvolvida. A validação da ferramenta foi feita através da realização de um estudo de caso por potenciais usuários, o que permitiu demonstrar a aplicabilidade da ferramenta e a da técnica desenvolvida. / This work presents Auto-FDI (Automatic Fault Detection and Isolation), a software tool for detection and diagnosis of faults in systems. The tool uses the analytical redundancy concept, where signals from the (possibly faulty) system are compared with expected signals from a model. The fault isolation employs a technique developed on this work, called automatic isolation. This technique uses a graph-based approach which considers the fault propagation and the lack of information about certain components of the system. Faults are pinpointed as accurately as possible given the level of detail in the model. In the scope of this work was addressed the whole process of specification, design, implementation and validation of the tool - used as proof of concept for the developed technique. The validation of the tool was made by conducting a case study for potential users, that has demonstrated the applicability of the tool and the technique developed.