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Dissertations / Theses on the topic 'Fault detection/estimation'
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Zhou, Yilun. "Fault detection and distributed estimation with sensor networks." Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/61021.
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A sensor network is a distributed system, consisting of plenty of embedded sensors that can be deployed over a large-scale physical environment. One of the major applications of sensor networks is to monitor the state of systems that are evolving in the sensing field. Thanks to the emergence of advancements in high-performance processors, nodes in a sensor network can not only collect measurements but coordinate to estimate the state of systems as well. This thesis proposes a monitoring architecture, where distributed state estimation and fault detection algorithms are implemented by every node in the sensor networks to track the system’s state while simultaneously detecting the faults occurred in either the monitored systems or the sensor networks. Several approaches for different monitoring tasks are presented in this thesis and classified into two main parts: distributed state estimation and fault detection algorithms in the monitoring architecture. In the first part, we present two distributed state estimation algorithms in the sensor networks for the monitoring of a system, which can be described by a centralized, decentralized, or distributed dynamic model. The first one is implemented over a sensor network, where the local estimator in each node consists of a filtering step – which uses a weighted combination of neighboring sensors information – and a model-based state predictor. The filtering weights and prediction parameters jointly minimize both the mean and variance of the prediction error in a Pareto optimization framework at each time step. Since each predictor uses the model of the whole system monitored by the sensor network, the algorithm over a sensor network can only monitor a centralized system or each subsystem of a decentralized system. For a distributed system, where several subsystems interact with each other, the second algorithm implemented over several sensor networks is introduced so that each sensor network can coordinate with neighboring networks to monitor the corresponding subsystem of the distributed system. The second part of the thesis is devoted to fault detection algorithms for process faults in monitored systems and sensor faults in sensor networks. The aforementioned estimation algorithm over a sensor network is applied to design process fault detection algorithm for a centralized or decentralized system. A residual is defined, and suitable stochastic thresholds are designed, allowing to set the parameters so to guarantee an upper bound of false alarms probability. For detecting sensor faults in the sensor networks, the centralized, decentralized, and distributed sensor fault detection schemes are proposed in a discrete-time framework. And the detection performance is compared by an industrial benchmark simulation in a continuous stirred tank heater (CSTH) pilot plant. Then a rigorous fault detectability and detection time interval analysis of the centralized sensor fault detection scheme is presented. The performance of proposed distributed estimation methods and effectiveness of presented fault detection methods are evaluated by extensive numerical and industrial benchmark simulations.
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Stocks, Mikael. "Stator fault detection and parameter estimation in induction machines." Licentiate thesis, Luleå, 2002. http://epubl.luth.se/1402-1757/2002/23.
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Xiong, Jun. "Set-membership state estimation and application on fault detection." Phd thesis, Institut National Polytechnique de Toulouse - INPT, 2013. http://tel.archives-ouvertes.fr/tel-01068054.
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La modélisation des systèmes dynamiques requiert la prise en compte d'incertitudes liées à l'existence inévitable de bruits (bruits de mesure, bruits sur la dynamique), à la méconnaissance de certains phénomènes perturbateurs mais également aux incertitudes sur la valeur des paramètres (spécification de tolérances, phénomène de vieillissement). Alors que certaines de ces incertitudes se prêtent bien à une modélisation de type statistique comme par exemple les bruits de mesure, d'autres se caractérisent mieux par des bornes, sans autre attribut. Dans ce travail de thèse, motivés par les observations ci-dessus, nous traitons le problème de l'intégration d'incertitudes statistiques et à erreurs bornées pour les systèmes linéaires à temps discret. Partant du filtre de Kalman Intervalle (noté IKF) développé dans [Chen 1997], nous proposons des améliorations significatives basées sur des techniques récentes de propagation de contraintes et d'inversion ensembliste qui, contrairement aux mécanismes mis en jeu par l'IKF, permettent d'obtenir un résultat garanti tout en contrôlant le pessimisme de l'analyse par intervalles. Cet algorithme est noté iIKF. Le filtre iIKF a la même structure récursive que le filtre de Kalman classique et délivre un encadrement de tous les estimés optimaux et des matrices de covariance possibles. L'algorithme IKF précédent évite quant à lui le problème de l'inversion des matrices intervalles, ce qui lui vaut de perdre des solutions possibles. Pour l'iIKF, nous proposons une méthode originale garantie pour l'inversion des matrices intervalle qui couple l'algorithme SIVIA (Set Inversion via Interval Analysis) et un ensemble de problèmes de propagation de contraintes. Par ailleurs, plusieurs mécanismes basés sur la propagation de contraintes sont également mis en oeuvre pour limiter l'effet de surestimation due à la propagation d'intervalles dans la structure récursive du filtre. Un algorithme de détection de défauts basé sur iIKF est proposé en mettant en oeuvre une stratégie de boucle semi-fermée qui permet de ne pas réalimenter le filtre avec des mesures corrompues par le défaut dès que celui-ci est détecté. A travers différents exemples, les avantages du filtre iIKF sont exposés et l'efficacité de l'algorithme de détection de défauts est démontré.
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Shafiei, Mehdi. "Distribution network state estimation, time dependency and fault detection." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/124659/2/Mehdi_Shafiei_Thesis.pdf.
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In this research work, the combination of three novel approaches is established to estimate the states of three-phase balanced and unbalanced distribution networks and using the developed methods for high impedance fault detection. The effectiveness of the developed methods are proposing a fast real-time state estimator with a low number of measurement devices, avoiding bad data detection in state estimation, and dynamically updating fault current thresholds to detect high impedance faults in the distribution networks.
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Yang, Zaiyue, and 楊再躍. "Fault detection, estimation and control of periodically excited nonlinear systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B40887984.
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Yang, Zaiyue. "Fault detection, estimation and control of periodically excited nonlinear systems." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B40887984.
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Su, Jinya. "Fault estimation algorithms : design and verification." Thesis, Loughborough University, 2016. https://dspace.lboro.ac.uk/2134/23231.
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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.
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Salehpour, Soheil. "Fault detection and model quality estimation using mixed integer linear programming /." Luleå : Luleå University of Technology, 2009. http://pure.ltu.se/ws/fbspretrieve/2740260.
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Zhang, Xiaoxia. "Incipient anomaly detection and estimation for complex system health monitoring." Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG025.
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La détection et le diagnostic des défauts naissants pour les systèmes d’ingénierie ou industriels multivariés à bruit élevé sont abordés dans ce travail de thèse par l’intermédiare d’une approche statistique non paramétrique ’globale’.Un défaut naissant induit un changement anormal dans les valeurs mesurées de la variable du système. Cependant, un tel changement est faible, et tend à ne pas causer de changements évidents dans les paramètres des distributions des signaux du système. En particulier dans un environnement bruité, les caractéristiques de ces défaults faible peuvent être masquées par le bruit et rend celui-ci difficile à évaluer. Dans une telle situation, l’utilisation de méthodes paramétriques traditionnelles pour la détection échouent. Pour faire face à ces difficultés et effectuer la détection et le diagnostic des défauts, une approche’globale’ qui peut prendre en compte la signature totale des défauts est nécessaire. La détection de défauts naissants peut être obtenue par la mesure des différences entre les distributions avant et après l’apparition du défaut. Certaines méthodes basées sur la distribution (dites ’globales’) ont été proposées, mais les performances de détection de ces approches existantes dans un environnement à haut niveau de bruit devraient être améliorées. Dans ce contexte, la divergence de Jensen-Shannon est considérée comme un indicateur de défaut ’global’ pour effectuer la détection et le diagnostic de défaut naissant dans un environnement à haut niveau de bruit. Ses performances de détection pour de petites variations anormales noyées dans le bruit sont validés en simulation. En outre, le problème de l’estimation des défauts est également étudié dans ce travail. Un modèle théorique d’estimation de la sévérité des défauts à parti dépend de la valeur de la divergence pour des conditions Gaussiennes est établi. La précision du modèle d’estimation est évaluée sur des modèles numériques par le biais de simulations. Ensuite, l’approche statistique ’globale’ est mise en oeuvre pour à deux applications dans le domaine de l’ingénierie. La première concerne la détection de fissures naissantes dans un matériau conducteur. La divergence de Jensen-Shannon combinée à l’analyse en composantes indépendantes et à la décomposition on ondelettes a été appliquée à la détection et à la caractérisation de fissures mineures dans des structures conductrices avec des perturbations bruit sur la base de signaux d’impédance expérimentaux. La deuxième application concerne le diagnostic de défauts naissants dans un processus non linéaire multivarié avec un bruit élevé. Le ’Tennessee Eastman Process’ (TEP) est un processus non linéaire multivarié typique pour lequel nous avons appliqué, la divergence de Jensen-Shannon combinée à l’analyse en composantes principales à noyau (ACPN) est pour étudier la détection de défauts naissants dont les difficultés de sont largement décrites dans la littératureIncipient fault detection and diagnosis in engineering and multivariate industrial systems with a high-level noise are addressed in this Ph.D. thesis by a ’global’ non-parametric statistical approach. An incipient fault is supposed to induce an abnormal change in the measured value of the system variable. However, such change is weak, and it tends not to cause obvious changes in the signal distribution’s parameters. Especially in high noise level environment, the weak fault feature can be masked by the noise and becomes unpredictable. In such a condition, using traditional parametric-based methods generally fails in the fault detection. To cope with incipient fault detection and diagnosis, a ’global’ approach that can consider the total faults signature is needed. The incipient fault detection can be obtained by measuring the differences between the signal distributions before and after the fault occurrence. Some distribution-based ’global’ methods have been proposed, however, the detection capabilities of these existed approaches in high noise level environment should be improved. In this context, Jensen-Shannon divergence is considered a ’global’ fault indicator to deal with the incipient fault detection and diagnosis in a high noise level environment. Its detection performance for small abnormal variations hidden in noise is validated through simulation. In addition, the fault estimation problem is also considered in this work. A theoretical fault severity estimation model depending on the divergence value for the Gaussian condition is derived. The accuracy of the estimation model is evaluated on numerical models through simulations. Then, the ’global’ statistical approach is applied to two applications in engineering. The first one relates to non- destruction incipient cracks detection. The Jensen-Shannon divergence combined with Noisy Independent Component Analysis and Wavelet analysis was applied for detection and characterization of minor cracks in conductive structures with high-level perturbations based on experimental impedance signals. The second application addresses the incipient fault diagnosis in a multivariate non-linear process with a high-level noise. Tennessee Eastman Process (TEP) is one typical multivariate non-linear process, the Jensen-Shannon divergence in the Kernel Principal Component Analysis (KPCA) is developed for coping with incipient fault detection in this process
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Törnqvist, David. "Statistical Fault Detection with Applications to IMU Disturbances." Licentiate thesis, Linköping University, Linköping University, Automatic Control, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7094.
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This thesis deals with the problem of detecting faults in an environment where the measurements are affected by additive noise. To do this, a residual sensitive to faults is derived and statistical methods are used to distinguish faults from noise. Standard methods for fault detection compare a batch of data with a model of the system using the generalized likelihood ratio. Careful treatment of the initial state of the model is quite important, in particular for short batch sizes. One method to handle this is the parity-space method which solves the problem by removing the influence of the initial state using a projection.
In this thesis, the case where prior knowledge about the initial state is available is treated. This can be obtained for example from a Kalman filter. Combining the prior estimate with a minimum variance estimate from the data batch results in a smoothed estimate. The influence of the estimated initial state is then removed. It is also shown that removing the influence of the initial state by an estimate from the data batch will result in the parity-space method. To model slowly changing faults, an efficient parameterization using Chebyshev polynomials is given.
The methods described above have been applied to an Inertial Measurement Unit, IMU. The IMU usually consists of accelerometers and gyroscopes, but has in this work been extended with a magnetometer. Traditionally, the IMU has been used to estimate position and orientation of airplanes, missiles etc. Recently, the size and cost has decreased making it possible to use IMU:s for applications such as augmented reality and body motion analysis. Since a magnetometer is very sensitive to disturbances from metal, such disturbances have to be detected. Detection of the disturbances makes compensation possible. Another topic covered is the fundamental question of observability for fault inputs. Given a fixed or linearly growing fault, conditions for observability are given.
The measurements from the IMU show that the noise distribution of the sensors can be well approximated with white Gaussian noise. This gives good correspondence between practical and theoretical results when the sensor is kept at rest. The disturbances for the IMU can be approximated using smooth functions with respect to time. Low rank parameterizations can therefore be used to describe the disturbances. The results show that the use of smoothing to obtain the initial state estimate and parameterization of the disturbances improves the detection performance drastically.
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Bradley, William J. "Current Based Fault Detection and Diagnosis of Induction Motors. Adaptive Mixed-Residual Approach for Fault Detection and Diagnosis of Rotor, Stator, Bearing and Air-Gap Faults in Induction Motors Using a Fuzzy Logic Classifier with Voltage and Current Measurement only." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/7265.
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Induction motors (IM) find widespread use in modern industry and for this reason they have been subject to a significant amount of research interest in recent times. One particular aspect of this research is the fault detection and diagnosis (FDD) of induction motors for use in a condition based maintenance (CBM) strategy; by effectively tracking the condition of the motor, maintenance action need only be carried out when necessary. This type of maintenance strategy minimises maintenance costs and unplanned downtime. The benefits of an effective FDD for IM is clear and there have been numerous studies in this area but few which consider the problem in a practical sense with the aim of developing a single system that can be used to monitor motor condition under a range of different conditions, with different motor specifications and loads.
This thesis aims to address some of these problems by developing a general FDD system for induction motor. The solution of this problem involved the development and testing of a new approach; the adaptive mixed-residual approach (AMRA). The main aim of the AMRA system is to avoid the vast majority of unplanned failures of the machine and therefore as opposed to tackling a single induction motor fault, the system is developed to detect all four of the most statistically prevalent induction motor fault types; rotor fault, stator fault, air-gap fault and bearing fault. The mixed-residual fault detection algorithm is used to detect these fault types which includes a combination of spectral and model-based techniques coupled with particle swarm optimisation (PSO) for automatic identification of motor parameters. The AMRA residuals are analysed by a fuzzy-logic classifier and the system requires only current and voltage inputs to operate. Validation results indicate that the system performs well under a range of load torques and different coupling methods proving it to have significant potential for use in industrial applications.The full-text was made available at the end of the embargo period on 29th Sept 2017.
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Qian, Lu [Verfasser]. "Observer-Based Fault Detection and Estimation of Rolling Element Bearing Systems / Lu Qian." Düren : Shaker, 2019. http://d-nb.info/1190525682/34.
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Bradley, William John. "Current based fault detection and diagnosis of induction motors : adaptive mixed-residual approach for fault detection and diagnosis of rotor, stator, bearing and air-gap faults in induction motors using a fuzzy logic classifier with voltage and current measurement only." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/7265.
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Induction motors (IM) find widespread use in modern industry and for this reason they have been subject to a significant amount of research interest in recent times. One particular aspect of this research is the fault detection and diagnosis (FDD) of induction motors for use in a condition based maintenance (CBM) strategy; by effectively tracking the condition of the motor, maintenance action need only be carried out when necessary. This type of maintenance strategy minimises maintenance costs and unplanned downtime. The benefits of an effective FDD for IM is clear and there have been numerous studies in this area but few which consider the problem in a practical sense with the aim of developing a single system that can be used to monitor motor condition under a range of different conditions, with different motor specifications and loads. This thesis aims to address some of these problems by developing a general FDD system for induction motor. The solution of this problem involved the development and testing of a new approach; the adaptive mixed-residual approach (AMRA). The main aim of the AMRA system is to avoid the vast majority of unplanned failures of the machine and therefore as opposed to tackling a single induction motor fault, the system is developed to detect all four of the most statistically prevalent induction motor fault types; rotor fault, stator fault, air-gap fault and bearing fault. The mixed-residual fault detection algorithm is used to detect these fault types which includes a combination of spectral and model-based techniques coupled with particle swarm optimisation (PSO) for automatic identification of motor parameters. The AMRA residuals are analysed by a fuzzy-logic classifier and the system requires only current and voltage inputs to operate. Validation results indicate that the system performs well under a range of load torques and different coupling methods proving it to have significant potential for use in industrial applications.
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Ru, Jifeng. "Adaptive estimation and detection techniques with applications." ScholarWorks@UNO, 2005. http://louisdl.louislibraries.org/u?/NOD,285.
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Thesis (Ph. D.)--University of New Orleans, 2005.Title from electronic submission form. "A dissertation ... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Engineering and Applied Science"--Dissertation t.p. Vita. Includes bibliographical references.
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Peng, Wei. "Fast Modelling, Torque-Ripple-Reduction and Fault-Detection Control of Switched Reluctance Motors." Doctoral thesis, Universite Libre de Bruxelles, 2019. https://dipot.ulb.ac.be/dspace/bitstream/2013/285757/5/contratWP.pdf.
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As the world moves towards a cleaner and greener future, electrical machines for various industrial purposes and transport applications have gained a lot of attention. Permanent magnet synchronous machines (PMSMs) are usually the solution for electric vehicle (EV) applications thanks to their high efficiency, compactness and high-power density. On the downside, although the price of rare-earth materials has recovered close to historical levels, concerns still remain and the questions on the environmental sustainability of these materials have also been raised, which has encouraged the researchers to consider rare-earth-free machines.The switched reluctance machine (SRM) is one of the competitive alternatives, thanks to the simple and robust construction, high reliability and inherent fault tolerance capability. However, it has a bad reputation when it comes to torque ripple and acoustic noise. And the highly nonlinear characteristic brings much difficulty to routine design purposes and machine optimisation.Therefore, some of the above mentioned problems are addressed - a torque-ripple-reduction, reliable and low-cost system of SRMs is presented in this thesis. Firstly from the modelling point of view, a combined magnetic equivalent circuit (MEC) and finite element (FE) model of SRMs is developed for fast characterization the nonlinear behavior. Secondly from the control point of view, various torque-ripple reduction techniques are implemented and compared. Moreover, a minimal current sensing strategy with enhanced fault-detection capability is proposed and validated experimentally. It requires two current sensors, to replace the phase current sensors, with no additional devices for fault detection, to achieve a more compact and low-cost drive. Finally from the reliability point of view, an interturn short-circuit fault detection method and a rotor position estimation approach are investigated and validated experimentally, which leads to a more reliable system.Doctorat en Sciences de l'ingénieur et technologie
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Fani, Mehran. "Fault diagnosis of an automotive suspension system." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016.
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With the development of the embedded application and driving assistance systems, it becomes relevant to develop parallel mechanisms in order to check and to diagnose these new systems. In this thesis we focus our research on one of this type of parallel mechanisms and analytical redundancy for fault diagnosis of an automotive suspension system. We have considered a quarter model car passive suspension model and used a parameter estimation, ARX model, method to detect the fault happening in the damper and spring of system.
Moreover, afterward we have deployed a neural network classifier to isolate the faults and identifies where the fault is happening. Then in this regard, the safety measurements and redundancies can take into the effect to prevent failure in the system.
It is shown that The ARX estimator could quickly detect the fault online using the vertical acceleration and displacement sensor data which are common sensors in nowadays vehicles. Hence, the clear divergence is the ARX response make it easy to deploy a threshold to give alarm to the intelligent system of vehicle and the neural classifier can quickly show the place of fault occurrence.
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Carbone, Marc A. Carbone. "Development of a Supervisory Tool for Fault Detection and Diagnosis of DC Electric Power Systems with the Application of Deep Space Vehicles." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1601984256665471.
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Boem, Francesca. "Distributed Methods for Estimation and Fault Diagnosis: the case of Large-scale Networked Systems." Doctoral thesis, Università degli studi di Trieste, 2013. http://hdl.handle.net/10077/8534.
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2011/2012L’obiettivo di questa tesi è il monitoraggio di sistemi complessi a larga-scala. L’importanza di questo argomento è dovuto alla rinnovata enfasi data alle problematiche riguardanti la sicurezza e l’affidabilità dei sistemi, diventate requisiti fondamentali nella progettazione. Infatti, la crescente complessità dei moderni sistemi, dove le relazioni fra i diversi componenti, con il mondo esterno e con il fattore umano sono sempre più importanti, implica una crescente attenzione ai rischi e ai costi dovuti ai guasti e lo sviluppo di approcci nuovi per il controllo e il monitoraggio. Mentre nel contesto centralizzato i problemi di stima e di diagnostica di guasto sono stati ampiamente studiati, lo sviluppo di metodologie specifiche per sistemi distribuiti, larga scala o “networked”, come i Cyber-Physical Systems e i Systems-of-Systems, è cominciato negli ultimi anni. Il sistema fisico è rappresentato come l’interconnessione di sottosistemi ottenuti attraverso una decomposizione del sistema complesso dove le sovrapposizioni sono consentite. L’approccio si basa sul modello dinamico non-lineare dei sottosistemi e sull’approssimazione adattativa delle non note interconnessioni fra i sottosistemi. La novità è la proposta di un’architettura unica che tenga conto dei molteplici aspetti che costituiscono i sistemi moderni, integrando il sistema fisico, il livello sensoriale e il sistema di diagnostica e considerando le relazioni fra questi ambienti e le reti di comunicazione. In particolare, vengono proposte delle soluzioni ai problemi che emergono dall’utilizzo di reti di comunicazione e dal considerare sistemi distribuiti e networked. Il processo di misura è effettuato da un insieme di reti di sensori, disaccoppiando il livello fisico da quello diagnostico e aumentando in questo modo la scalabilità e l’affidabilità del sistema diagnostico complessivo. Un nuovo metodo di stima distribuita per reti di sensori è utilizzato per filtrare le misure minimizzando sia la media sia la varianza dell’errore di stima attraverso la soluzione di un problema di ottimizzazione di Pareto. Un metodo per la re-sincronizzazione delle misure è proposto per gestire sistemi multi-rate e misure asincrone e per compensare l’effetto dei ritardi nella rete di comunicazione fra sensori e diagnostici. Poiché uno dei problemi più importanti quando si considerano sistemi distribuiti e reti di comunicazione è per l’appunto il verificarsi di ritardi di trasmissione e perdite di pacchetti, si propone una strategia di compensazione dei ritardi , basata sull’uso di Time Stamps e buffer e sull’introduzione di una matrice di consenso tempo-variante, che permette di gestire il problema dei ritardi nella rete di comunicazione fra diagnostici. Gli schemi distribuiti per la detection e l’isolation dei guasti sono sviluppati, garantendo la convergenza degli stimatori e derivando le condizioni sufficienti per la detectability e l’isolability. La matrice tempo-variante proposta permette di migliorare queste proprietà definendo delle soglie meno conservative. Alcuni risultati sperimentali provano l’efficacia del metodo proposto. Infine, le architetture distribuite per la detection e l’isolation, sviluppate nel caso tempo-discreto, sono estese al caso tempo continuo e nello scenario in cui lo stato non è completamente misurabile, sia a tempo continuo che a tempo discreto.
This thesis deals with the problem of the monitoring of modern complex systems. The motivation is the renewed emphasis given to monitoring and fault-tolerant systems. In fact, nowadays reliability is a key requirement in the design of technical systems. While fault diagnosis architectures and estimation methods have been extensively studied for centralized systems, the interest towards distributed, networked, large-scale and complex systems, such as Cyber-Physical Systems and Systems-of-Systems, has grown in the recent years. The increased complexity in modern systems implies the need for novel tools, able to consider all the different aspects and levels constituting these systems. The system being monitored is modeled as the interconnection of several subsystems and a divide et impera approach allowing overlapping decomposition is used. The local diagnostic decision is made on the basis of the knowledge of the local subsystem dynamic model and of an adaptive approximation of the uncertain interconnection with neighboring subsystems. The goal is to integrate all the aspects of the monitoring process in a comprehensive architecture, taking into account the physical environment, the sensor layer, the diagnosers level and the communication networks. In particular, specifically designed methods are developed in order to take into account the issues emerging when dealing with communication networks and distributed systems. The introduction of the sensor layer, composed by a set of sensor networks, allows the decoupling of the physical and the sensing/computation topologies, bringing some advantages, such as scalability and reliability of the diagnosis architecture. We design the measurements acquisition task by proposing a distributed estimation method for sensor networks, able to filter measurements so that both the variance and the mean of the estimation error are minimized by means of a Pareto optimization problem. Moreover, we consider multi-rate systems and non synchronized measurements, having in mind realistic applications. A re-synchronization method is proposed in order to manage the case of multi-rate systems and to compensate delays in the communication network between sensors and diagnosers. Since one of the problems when dealing with distributed, large-scale or networked systems and therefore with a communication network, is inevitably the presence of stochastic delays and packet dropouts, we propose therefore a distributed delay compensation strategy in the communication network between diagnosers, based on the use of Time Stamps and buffers and the definition of a time-varying consensus matrix. The goal of the novel time-varying matrix is twofold: it allows to manage communication delays, packet dropouts and interrupted links and to optimize detectability and isolability skills by defining less conservative thresholds. The distributed fault detection and isolation schemes are studied and analytical results regarding fault detectability, isolability and estimator convergence are derived. Simulation results show the effectiveness of the proposed architecture. For the sake of completeness, the monitoring architecture is studied and adapted to different frameworks: the fault detection and isolation methodology is extended for continuous-time systems and the case where the state is only partially measurable is considered for discrete-time and continuous-time systems.
XXV Ciclo
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Boche, Adèle. "Méthodes indirectes d'adaptation et de décision pour la sécurisation du vol des drones à voilure fixe." Thesis, Toulouse, ISAE, 2018. http://www.theses.fr/2018ESAE0043/document.
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De par l’augmentation de leur utilisation, la sécurisation du vol des drones devient de plus en plus importante. La commande tolérante aux fautes peut alors contribuer à l’obtention d’un niveau de sécurité acceptable. Le but de cette thèse est de développer une méthode de commande tolérante aux fautes basée sur deux types d’approches : l’approche Automatique qui utilise une représentation de systèmes à l’aide de modèles décrivant des évolutions continues et l’approche Intelligence Artificielle qui se base sur la représentation de systèmes à l’aide de modèles discrets ou logiques. Ainsi la première contribution de cette recherche est le développement d'une méthode générique de commande tolérante aux fautes utilisant les cadres de modélisation discret et continu. L’idée consiste à combiner une modélisation continue permettant d’estimer l’état et les paramètres de fautes et une modélisation discrète permettant de prendre une décision en ligne quant au contrôleur à utiliser. L’estimation continue permet d’avoir plus d’informations sur la faute qu’avec une modélisation discrète, alors que celle-ci prend en compte des probabilités de panne et des techniques d’optimisation qui sont plus adaptées à la tâche de décision. La seconde contribution concerne le développement et la validation d’une méthode permettant de détecter et de diagnostiquer la faute. Pour ses avantages, l’idée a été de développer un filtre de Kalman sensibles aux sauts de panne pour l’estimation de l’état et des paramètres de fautes. Pour la détection et le diagnostic de la panne, l’idée a été d’utiliser les données de l’estimation de façon probabiliste. Une fois la faute détectée et identifiée, le système de commande doit réagir pour pouvoir compenser cette faute. La troisième contribution porte donc sur l’amélioration du suivi de la trajectoire par reconfiguration du système de commande. L’objectif est de combiner les méthodes de commutation et d’adaptation, afin de limiter le nombre de contrôleurs en utilisant des contrôleurs adaptatifs pour les modes dégradés, tout en ayant des contrôleurs faciles à concevoir. Des techniques d’optimisation sont alors utilisées de façon à prendre une décision en ligne quant au choix du contrôleur. Finalement, la méthode développée doit être vérifiée avant de pouvoir être implémentée sur un drone. La dernière contribution est l’évaluation de la capacité de la méthode à suivre une trajectoire d’atterrissage en cas de pannes capteurs ou actionneurs grâce à un modèle de droneMajor security risks appear with the increase of the number of UAV in the air space. Thus, UAV security is more and more important and Fault Tolerant Control (FTC) methods could support the achievement of acceptable security level. The aims of this research is to develop a FTC method which combines two approaches : Automatic Control approach which is based on model which have a continuous representation of the system and Artificial Intelligence approach which is based on discrete or logical model to represent the system. Thus, the first contribution of this thesis is the development of a generic fault tolerant control method which uses discrete and continuous frameworks. The idea was to combine a continuous framework to estimate the state and fault parameters and a discrete framework to take on line a decision about the controller. The continuous estimation provides more knowledge on the fault whereas a discrete model allows the use of different optimization tools which are more adapted to decision task. The second contribution is the development and the validation of a method for fault detection and diagnosis. For its potential, a Kalman filter is adapted in order to be sensitive to abrupt faults and used for state and fault parameters estimation. These estimates are then used in a probabilistic way to detect and identify the fault. Once the fault is detected, the control system should react to compensate the fault. Thus, the third contribution of this thesis is the improvement of the trajectory tracking by reconfiguration of the control system. The aim is to combine switching and adaptive methods in order to limit the number of controllers by using adaptive controllers for degraded modes while having convenient controllers. Optimization tools are then used to take the decision on the controller to use. Finally, the method has to be validated before being implemented on line. The last contribution is the evaluation of the ability of the method to follow its trajectory despite the apparition of actuator or sensor faults during a landing approach
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Farhat, Ahmad. "Détection, localisation et estimation de défauts : application véhicule." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAT056/document.
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Dans la nécessité de développer des véhicules sûrs, confortables, économiques et à faible impact environnemental, les voitures sont de plus en plus équipées d'organes qui emploient des capteurs, actionneurs et systèmes de commande automatiques.Or ces systèmes, critiques pour la sécurité et le confort des passagers, peuvent mal-fonctionner en présence d'une défaillance (défaut).Dans le cadre du diagnostic à bord, plusieurs approches à base de modèle sont développées dans ce travail afin de détecter, localiser et estimer un défaut capteur ou actionneur, et pour détecter la perte de stabilité du véhicule.Ces méthodes reposent sur une synthèse robuste pour les systèmes incertains à commutation.Elles sont validées en simulation avec le logiciel CarSim, et sur les données réelles de véhicule dans le cadre du projet INOVEModern vehicles are increasingly equipped with new mechanisms to improve safety, comfort and ecological impact. These active systems employ sensors, actuators and automatic control systems. However, in case of failure of one these components, the consequences for the vehicle and the passengers safety could be dramatic. In order to ensure a higher level of reliability within on board diagnosis, new methodologies for sensor or actuator fault detection, location and estimation are proposed. These model based approaches are extended for robust synthesis for switched uncertain systems. In addition, a method for detecting critical stability situation is presented. The validation of the different methods is illustrated with simulations using CarSim, and application on real vehicle data within the INOVE project
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López, Estrada Francisco Ronay. "Model-based fault diagnosis observer design for descriptor LPV system with unmeasurable gain scheduling." Thesis, Université de Lorraine, 2014. http://www.theses.fr/2014LORR0162/document.
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Ce mémoire de thèse est consacré à la conception de méthodes de diagnostic à base de modèles fondées sur les observateurs pour les systèmes non linéaires modélisés comme des systèmes singuliers (D) linéaires à paramètres variants (LPV), notés D-LPV (Descriptor-Linear Parameter Varying). Les systèmes D-LPV constituent une classe particulière de systèmes approximant avec un certain degré de précision la dynamique des systèmes complexes non linéaires à partir d’une combinaison de modèles linéaires locaux pondérés par des fonctions convexes d'ordonnancement. Dans le contexte de l’apparition de défauts capteurs ou actionneurs, ce travail de thèse s’attache aux systèmes pour lesquels ces fonctions d'ordonnancement sont non mesurables mais dépendent de l'état du système. Afin de détecter et isoler des défauts, ce travail de thèse développe des synthèses d'observateurs appropriés en développant des nouvelles conditions suffisantes en termes d’inégalités matricielles linéaires (LMI) pour garantir la synthèse de résidus sensibles aux défauts et robustes aux erreurs d’estimation inhérentes aux fonctions d'ordonnancement non mesurables. - Étendant des méthodes H∞ afin d’effectuer l'estimation d'état, la détection de pannes, la localisation et la reconstruction de défaut sur les capteurs ; - Garantissant une sensibilité “optimale” aux pannes vis-À-Vis du rejet de perturbations au travers le développement d’observateurs de type H_/H∞. À cette fin, le mémoire de thèse est organisé en cinq chapitres : Le Chapitre 1 est consacré à l’introduction générale, aux objectifs et contributions de ce travail. Le Chapitre 2 présente les éléments nécessaires pour décrire la représentation, la modélisation, les propriétés, l'analyse et la conception d'observateur pour les systèmes D-LPV ainsi qu’un état de l’art détaillé des travaux associés à ce thème de recherche. Le Chapitre 3 est dédié au développement de trois méthodes différentes fondées sur la théorie H∞ pour concevoir des observateurs de détection de défaut pour les systèmes D-LPV. Les méthodes proposées sont appliquées à un exemple dans le cadre de la détection de défaut capteurs. L’isolation de ces défauts est mise en œuvre au travers un banc d’observateurs et les performances de chacune des trois méthodes sont comparées. Le Chapitre 4 propose une méthode de détection de défauts sur la base d’observateurs établis sur le principe H_/H∞, tenant compte ainsi d'un meilleur compromis entre la sensibilité aux pannes et la robustesse aux perturbations. De nouvelles conditions suffisantes à l’aide de LMI sont proposées afin de résoudre le problème de synthèse du gain des observateurs. Le dernier chapitre est dédié à la conclusion générale et à l’analyse de problèmes ouverts pouvant être abordés dans des travaux futursThis work is dedicated to the synthesis of model-Based fault detection and isolation (FDI) techniques based on observers for nonlinear systems modeled as Descriptor-Linear Parameter Varying (D-LPV) systems. D-LPV systems are a particular class of systems that can represent (or approximate in some degree of accuracy), complex nonlinear systems by a set of linear local models blended through convex parameter-Dependent scheduling functions. The global D-LPV System can describe both time-Varying and nonlinear behavior. Nevertheless, in many applications the time-Varying parameters in the scheduling functions could be unmeasurable. Models which depend on unmeasurable scheduling functions cover a wide class of nonlinear systems compared to models with measurable scheduling functions, but the design of control schemes for D-LPV systems with unmeasurable scheduling functions are more difficult than those with a measurable one, because the design of such control schemes involve the estimation of the scheduling vector. This topic is addressed in this work by considering the following main targets: • to design FDI in D-LPV systems based on -H∞ observers in order to guarantee robustness against disturbances and errors due the unmeasurable gain scheduling functions • to extend the proposed -H∞ methods to perform state estimation and fault detection, isolation and fault magnitude estimation in the case of sensor faults • to guarantee the best trade-Off between fault sensitivity and disturbance rejection by developing H_/H∞ fault detection observers for D-LPV systems. The thesis is organized as follows Chapter 1 is dedicated to provide a general introduction, the objectives and contribution of this work.Chapter 2 is organized in order to provide the minimum necessary elements to describe the representation, modeling, properties, analysis, and observer design of D-LPV systems. Chapter 2 is also dedicated to a detailed review of the state of the art. Chapter 3 is dedicated to the development of three different methods to design fault detection observers for D-LPV systems based on H∞ theory. Finally, the proposed methods are applied to an example, for sensor fault detection and isolation by means of an observer bank, in order to compare the performance of each method. Chapter 4 is dedicated to the design of a FDI method based on observers with H_/H∞ performance. Based on the H_/H∞ approach, which considers the best trade-Off between fault sensitivity and robustness to disturbance, adequate LMIs are obtained to guarantee sufficient conditions for the design problem. In order to illustrate the effectiveness of the proposed techniques, an example is considered
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Farfan-Ramos, Luis. "Real-time Fault Diagnosis of Automotive Electrical Power Generation and Storage System." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1303129393.
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Harrysson, Mattias. "Fault Location Algorithms in Transmission Grids." Thesis, Högskolan i Halmstad, Sektionen för ekonomi och teknik (SET), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-26314.
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The rapid growth of the electric power system has in recent decades resulted in an increase of the number of transmission lines and total power outage in Norway. The challenge of a fast growing electrical grid has also resulted in huge increases of overhead lines and their total length. These lines are experiencing faults due to various reasons that cause major disruptions and operating costs of the transmission system operator (TSO). Thus, it’s important that the location of faults is either known or can be estimated with reasonably high accuracy. This allows the grid owner to save money and time for inspection and repair, as well as to provide a better service due to the possibility of faster restoration of power supply and avoiding blackouts. Fault detection and classification on transmission lines are important tasks in order to protect the electrical power system. In recent years, the power system has become more complicated under competitive and deregulated environments and a fast fault location technique is needed to maintain security and supply in the grid. This thesis compares and evaluates different methods for classification of fault type and calculation of conventional one-side and two-side based fault location algorithms for distance to fault estimation. Different algorithm has been implemented, tested and verified to create a greater understanding of determinants facts that affect distance to faults algorithm’s accuracy. Implemented algorithm has been tested on the data generated from a number of simulations in Simulink for a verification process in implemented algorithms accuracy. Two types of fault cases have also been simulated and compared for known distance to fault estimation.
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Costa, Bruno Sielly Jales. "Detec??o e diagn?stico de falhas n?o-supervisionados baseados em estimativa de densidade recursiva e classificador fuzzy auto-evolutivo." Universidade Federal do Rio Grande do Norte, 2014. http://repositorio.ufrn.br:8080/jspui/handle/123456789/18577.
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In this work, we propose a two-stage algorithm for real-time fault detection and identification of industrial plants. Our proposal is based on the analysis of selected features using recursive density estimation and a new evolving classifier algorithm. More specifically, the proposed approach for the detection stage is based on the concept of density in the data space, which is not the same as probability density function, but is a very useful measure for abnormality/outliers detection. This density can be expressed by a Cauchy function and can be calculated recursively, which makes it memory and computational power efficient and, therefore, suitable for on-line applications. The identification/diagnosis stage is based on a self-developing (evolving) fuzzy rule-based classifier system proposed in this work, called AutoClass. An important property of AutoClass is that it can start learning from scratch". Not only do the fuzzy rules not need to be prespecified, but neither do the number of classes for AutoClass (the number may grow, with new class labels being added by the on-line learning process), in a fully unsupervised manner. In the event that an initial rule base exists, AutoClass can evolve/develop it further based on the newly arrived faulty state data. In order to validate our proposal, we present experimental results from a level control didactic process, where control and error signals are used as features for the fault detection and identification systems, but the approach is generic and the number of features can be significant due to the computationally lean methodology, since covariance or more complex calculations, as well as storage of old data, are not required. The obtained results are significantly better than the traditional approaches used for comparison
Este trabalho prop?e um algoritmo de dois estagios para detec??o e identifica??o de falhas, em tempo real, em plantas industriais. A proposta baseia-se na analise de caracter?sticas selecionadas utilizando estimativa de densidade recursiva e um novo algoritmo evolutivo de classifica??o. Mais especificamente, a abordagem proposta para detec??o e baseada no conceito de densidade no espa?o de dados, o que difere da tradicional fun??o densidade de probabilidade, porem, sendo uma medida bastante util na detec??o de anormalidades/outliers. Tal densidade pode ser expressa por uma fun??o de Cauchy e calculada recursivamente, o que torna o algoritmo computacionalmente eficiente, em termos de processamento e memoria, e, dessa maneira, apropriado para aplica??es on-line. O estagio de identifica??o/diagnostico e realizado por um classificador baseado em regras fuzzy capaz de se auto-desenvolver (evolutivo), chamado de AutoClass, e introduzido neste trabalho. Uma propriedade importante do AutoClass e que ele e capaz de aprender a partir do zero". Tanto as regras fuzzy, quanto o numero de classes para o algoritmo n?o necessitam de pre-especifica??o (o numero de classes pode crescer, com os rotulos de classe sendo adicionados pelo processo de aprendizagem on-line), de maneira n~ao-supervisionada. Nos casos em que uma base de regras inicial existe, AutoClass pode evoluir/desenvolver-se a partir dela, baseado nos dados adquiridos posteriormente. De modo a validar a proposta, o trabalho apresenta resultados experimentais de simula??o e de aplica??es industriais reais, onde o sinal de controle e erro s?o utilizados como caracter?sticas para os estagios de detec??o e identifica??o, porem a abordagem e generica, e o numero de caracter?sticas selecionadas pode ser significativamente maior, devido ? metodologia computacionalmente eficiente adotada, uma vez que calculos mais complexos e armazenamento de dados antigos n?o s?o necess?rios. Os resultados obtidos s?o signifificativamente melhores que os gerados pelas abordagens tradicionais utilizadas para compara??o
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Arafat, AKM. "ANALYSIS AND CONTROL OF FIVE-PHASE PERMANENT MAGNET ASSISTED SYNCHRONOUS RELUCTANCE MOTOR DRIVE UNDER FAULTS." University of Akron / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=akron1524168102423576.
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Bousghiri, Souad. "Diagnostic de fonctionnement des procédés continus par réconciliation d'état généralisé : application à la détection de pannes de capteurs et d'actionneurs." Nancy 1, 1994. http://www.theses.fr/1994NAN10396.
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Les travaux présentés, dans ce mémoire, concernent l'étude de la détection et de la localisation de défauts de fonctionnement de capteurs ou d'actionneurs de systèmes continus en régime dynamique. Après un rappel sur la notion de détection, nous présentons différentes approches du diagnostic en insistant plus particulièrement sur les méthodes utilisant la rebondance analytique. Un bref aperçu de la théorie de la réconciliation de données est exposée. Différentes méthodes permettant une estimation simultanée des états et des commandes (état généralisé) sont présentées dans le cas général, c'est-à-dire dans le cas où toutes les grandeurs ne sont pas nécessairement mesurées. La solution proposée donne une estimation en temps diffère, car du point de vue temps et volume de calcul, son application directe s'avère inexploitable. A partir de la formulation du problème d'estimation, nous donnons des solutions adaptées à un traitement en ligne sous deux formes. La première forme montre que l'estimation est une fonction linéaire des mesures antérieures. La seconde forme est récursive, la solution à l'instant courant est fonction de celle obtenue à l'instant précédent. Pour cette seconde forme, nous utilisons la formulation des systèmes singuliers pour l'estimation de l'état et de la commande. Ces méthodes sont comparées sur des exemples permettant de mieux cerner les avantages et les inconvénients de chacune d'entre elles pour une application au diagnostic. Nous présentons ensuite, compte tenu du choix de l'estimateur à fenêtre glissante, une stratégie de détection et de localisation de défaut de capteurs ou d'actionneurs. La sensibilité de l'estimateur vis-à-vis des incertitudes de modèle est alors abordée. Nous développons ensuite une fonction de localisation de l'élément défaillant. L'application à deux systèmes réels: un pendule inversé et un procède hydraulique, montre les bonnes qualités de notre stratégie
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Lemarchand, Antoine. "Modélisation multi-modèle incertaine du trafic routier et suivi robuste de profils optimaux aux entrées des voies périurbaines." Thesis, Grenoble, 2011. http://www.theses.fr/2011GRENT117/document.
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Ce document synthétise mes travaux de thèse de doctorat en Automatique Productiqueà Grenoble INP (Institut National Polytechnique), thèse préparée au sein dudépartement automatique du laboratoire GIPSA-lab (Grenoble Image Parole Signal etAutomatique). Ce travail s’inscrit dans le cadre du contrôle local et de la supervisiondes systèmes de trafic routier. Les principales contributions portent sur la modélisation,la supervision et la commande locale des systèmes de trafic routier.La contribution apportée à la modélisation du trafic est l’ajout d’un modèle d’incertitudesur le modèle CTM (Cell Transmission Model [Daganzo, 1994]). Ce nouveaumodèle permet de prendre en compte les incertitudes sur différents paramètres dumodèle pour in-fine proposer de nouvelles stratégies de commandes commutées robustes.Outre cette approche de modélisation, nous proposons un niveau de supervisionpermettant d’une part d’estimer en temps réel le mode de fonctionnement et d’autrepart de détecter, localiser et estimer certaines fautes sur le système. L’estimation dynamiquede mode de fonctionnement nous permet de connaître l’état de congestion (ou denon-congestion) de l’aménagement routier considéré. Nous sommes en mesure de détecterdes fautes telles que des chutes de vitesse ou des chutes de capacité survenant sur la route.Enfin, nous proposons deux lois de commandes locales basées sur la théorie dessystèmes à commutations. Ainsi, le schéma de contrôle s’adaptera dynamiquementaux changements de propriétés du système. Ces lois de commande ont pour objet des’insérer dans un schéma de régulation hiérarchiqueThis document synthesizes my Phd thesis work in Automatic Control in Grenoble-INP. This thesis has been prepared in the automatic control department of thelaboratory GIPSA-lab. This work is situated in the area of traffic systems control andsupervision. Our contributions are about modeling, supervision and local traffic control.The CTM traffic model has been extended with a model of uncertainties. Thisnews model allows us to take into account the uncertain parameters of the model, topropose new robust switched control law.In addition to this modeling approach, we propose some developments on supervisionof trafic systems. On one hand, we can estimate the operating mode of thesystem in real time and on the other hand to estimate some faults on the system. Thedynamical estimation of the operating mode allows us to know the state of congestion(or non congestion) of the road. We are able to estimate faults such as speed fall andcapacities drop that may appear.Finally, we propose two control laws based on switching systems control. The developedcontrollers adapt their geometry to the properties of the system. The purposeof these controllers is to be inserted in a hierarchic control scheme
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Amri, Mohamed-Hédi. "Fusion ensembliste de donn´ees pour la surveillance des personnes d´ependantes en habitat intelligent." Thesis, Orléans, 2015. http://www.theses.fr/2015ORLE2030/document.
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Mes travaux de recherches en thèse s’inscrivent dans le cadre du projet FUIE-monitorâge. L’objectif du projet, réunissant de nombreux partenaires industriels et universitaires, est d’améliorer la prise en charge individualisée et la sécurité du résident dans les établissements d’hébergement pour personnes âgées dépendantes(EHPAD). Dans ce travail, nous avons élaboré une méthode de fusion de données multimodales issues des différents capteurs installés dans un smart home. Ces informations sont utilisées pour la localisation intérieure des personnes afin de surveiller leurs activités journalières. Généralement, les mesures issues des capteurs sont soumises à des incertitudes. Dans nos travaux, ces erreurs sont supposées inconnues mais bornées. En tenant compte de cette hypothèse, une méthode de résolution d’un problème d’estimation d’état est élaborée en se basant sur des calculs ensemblistes. Notre algorithme de filtrage ensembliste comporte deux étapes. La première, dite de prédiction, est basée sur l’utilisation d’un modèle de marche aléatoire avec des hypothèses minimales (vitesse de déplacement maximale) pour prédire la zone où se trouve la personne. La deuxième étape, dite de correction, consiste à utiliser la mesure pour affiner cette zone. Cette étape utilise une technique de propagation de contraintes relâchée, q-relaxed intersection, pour permettre une meilleure robustesse par rapport aux données aberrantes. Notre algorithme est capable de quantifier, par un intervalle, l’incertitude commise sur les positions de cibles en mouvement tout en détectant les défauts de capteursOur research work is a part of the project FUI 14 FEDER Collectivités E-monitor’âge. This project takes place within the framework of Ambient Assisted Living (AAL) which aims to improve the safety and the comfort of elderly people living in smart nursing homes. This work aims to monitor the activities of elderly persons using information from different sensors. The ADL (Activities of Daily Living) are used to evaluate the ability of the person to perform on their own a selection of the activities which are essential for an independent living in the everyday life. Generally, process knowledge and measurements coming from sensors are prone to indeterminable noise. In our work, we suppose that these errors are unknown but bounded. Taking into account this hypothesis, we show how to solve the estimation issue using set-membership computations techniques. Our algorithm, based on set-membership approach, consists of two steps. The prediction step, based on the use of a random walk mobility with minimum assumptions (maximum speed of moving), employs the previous state estimate to provide the prediction zone where the person may be located. The correction step uses the informations coming from the sensors to refine this predicted zone. This step uses a relaxed constraints propagation technique, q-relaxed intersection, to deal with faulty measurements. This proposed method allows us to compute the uncertainty domain for the reconstructed localization of moving targets as dealing with outliers
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Cannon, Brandon Jeffrey. "Fault Detection for Unmanned Aerial Vehicles with Non-Redundant Sensors." BYU ScholarsArchive, 2014. https://scholarsarchive.byu.edu/etd/5308.
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To operate, autonomous systems of necessity employ a variety of sensors to perceive their environment. Many small unmanned aerial vehicles (UAV) are unable to carry redundant sensors due to size, weight, and power (SWaP) constraints. Faults in these sensors can cause undesired behavior, including system instability. Thus, detection of faults in these non-redundant sensors is of paramount importance.The problem of detecting sensor faults in non-redundant sensors on board autonomous aircraft is non-trivial. Factors that make development of a solution difficult include both an inability to perfectly characterize systems and sensors as well as the SWaP constraints inherent with small UAV. An additional challenge is the ability of a fault-detection method to strike a balance between false-alarm rate and detection rate.This thesis explores two model-based methods of fault-detection for non-redundant sensors, a Kalman filter based method and a particle filter based method. The Kalman filter based method employs tests of mean and covariance on the normalized innovation sequence to detect faults, while the particle filter based method uses a function of the average particle weights.The Kalman filter based approach was implemented in real time on board an autonomous rotorcraft using an extended Kalman Filter (EKF). Faults tested included varied levels of bias, drift, and increased noise. Metrics included false-alarm rate, detection rate, and delay to detection. The particle filter based approach was implemented on a simulated system. This was then compared with an implementation of the EKF based approach for the same system. The same fault types and metrics were also used for these tests.The EKF based method of fault-detection performed well onboard the autonomous rotorcraft and should be generalizable to other systems for which an EKF or Kalman filter can be implemented. The theory indicates that the particle filter based algorithm should have performed better, though the simulations showed poor detection characteristics in comparison to the Kalman filter based method. Future work should be performed to explore improvements to the particle filter based method.
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Wang, Hao. "Two-Stage Fault Location Detection Using PMU Voltage Measurements in Transmission Networks." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/54565.
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Fault location detection plays a crucial role in power transmission network, especially on security, stabilization and economic aspects. Accurate fault location detection in transmission network helps to speed up the restoration time, therefore, reduce the outage time and improve the system reliability [1]. With the development of Wide Area Measurement System (WAMS) and Phasor Measurement Unit (PMU), various fault location algorithms have been proposed.
The purpose of this work is to determine, modify and test the most appropriate fault location method which can be implemented with a PMU only linear state estimator. The thesis reviews several proposed fault location methods, such as, one-terminal [2], multi-terminal [3]-[11] and travelling wavelets methods [12]-[13]. A Two-stage fault location algorithm using PMU voltage measurements proposed by Q. Jiang [14] is identified as the best option for adaption to operate with a linear state estimator. The algorithm is discussed in details and several case studies are made to evaluate its effectiveness. The algorithm is shown to be easy to implement and adapt for operation with a linear state estimator. It only requires a limited number of PMU measurements, which makes it more practical than other existing methods. The algorithm is adapted and successfully tested on a real linear state estimator monitored high voltage transmission network.Master of Science
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Rostaing, Gilles. "DIAGNOSTIC DE DÉFAUT DANS LES ENTRAINEMENTS ÉLECTRIQUES." Phd thesis, Grenoble INPG, 1997. http://tel.archives-ouvertes.fr/tel-00909645.
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Cette thèse représente une contribution aux études sur la disponiblité des dispositifs électrotechnique. L'étude présentée vise à définir la méthode de redondance analytique, basée sur l'estimation d'état, la mieux adaptée au diagnostic des entraînements électriques en considérant les défauts de l'ensemble du convertisseur, de la commande et des capteurs. La méthode retenue doit permettre d'obtenir un modèle de diagnostic implantable en temps réel et sans ajout de capteurs supplémentaires. L'application retenue est un entraînement à courant continu commandé en couple. Le chapitre II compare deux modèles analytiques nommés modèles parallèle et permet de retenir un modèle parallèle "découplé" qui permet une bonne détection et une bonne localisation des défauts d'électronique de puissance ainsi que des défauts capteur. Malheureusement les modèles parallèles sont dépendants des entrées perturbatrices du procédé. Les perturbations génèrent donc des fausses alarmes La batterie d'observateur à entrées inconnues mise au point au chapitre III permet de s'affranchir de l'entrée perturbatrice que constitue dans notre cas le couple de charge. Cette technique est moins dépendante, en terme de découplage, du système car l'injection de sortie grace à la matrice de gain permet de disposer de degrés de liberté supplémentaires qui autorisent un réglage des découplages et des sensibilités. Les observateurs (à entrées inconnues) sont donc, à priori, les modèles de diagnostic les mieux adaptés à la à la détection et la localisation de défauts dans les entrainements électriques à courant continu.
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Ait, Ladel Ayyoub. "Commande tolérante aux défauts des systèmes à multimodèles : application à des systèmes à multi-source d'énergie." Electronic Thesis or Diss., Aix-Marseille, 2022. http://theses.univ-amu.fr.lama.univ-amu.fr/220330_AITLADEL_882kmyqu655hkbca383zrkcz402ihwf_TH.pdf.
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Cette thèse traite des problématiques de détection/estimation des défauts et de commande tolérante aux défauts par conception intégrée. L’objectif est de concevoir des stratégies de contrôle capables de maintenir une performance robuste et acceptable même en présence de défauts. Les systèmes considérés sont de nature dynamique et sont à multi-modèles. Les approches proposées, dans le cadre de la thèse, consistent à formaliser la conception intégrée des unités de détection/estimation et de contrôle tolérant aux défauts sous la forme d’inégalités matricielles linéaires afin de surmonter la difficulté posée par le couplage observateur/contrôleur. Ces approches offrent la possibilité de considérer les différentes interactions entre le système, l’unité de détection/estimation et l’unité de contrôle. Cela permet d’assurer une analyse de stabilité globale du système en boucle fermée, et des performances robustes en termes de détection/estimation, de contrôle et de compensation des défauts. La thèse est composée principalement de trois parties. Dans la première partie, les résultats sur le contrôle, en présence de défauts capteurs ou actionneurs, sont établis pour les systèmes linéaires à commutation. Dans la deuxième partie, des extensions aux systèmes non-linéaires à commutation avec des défauts de capteurs et d’actionneurs sont proposées. Finalement, la troisième partie est constituée d’une étude complète d’un système à base d’énergies renouvelables. Il s’agit d’un système de nature multi-sources et multi-charges conçu pour répondre à plusieurs demandes et est assujetti à l’intermittence des énergies renouvelablesThis thesis deals with the fault detection/estimation and fault-tolerant control challenges through integrated design. The aim is to design control strategies able to maintain robust and acceptable performance even in the presence of faults. The considered systems are dynamic in nature and are multi-model. The approaches proposed in this thesis consist in formalizing the integrated design of the detection/estimation and fault-tolerant control units in the form of linear matrix inequalities to overcome the difficulty posed by the observer/controller coupling. These approaches provide the ability to consider the different interactions between the system, the detection/estimation unit, and the control unit. Therefore, ensuring a global stability analysis of the closed-loop system and robust performances in terms of detection/estimation, control, and fault compensation. The thesis is mainly composed of three parts. In the first part, results on the control in the presence of sensor or actuator faults are established for switched linear systems. In the second part, extensions to switched nonlinear systems with sensor and actuator faults are proposed. Finally, the third part consists of a complete study of a renewable energy system. It is a multi-source/multi-load system designed to meet multiple demands and is subject to the intermittency of renewable energies
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Abdellatif, Meriem. "Continuité de service des entraînements électriques pour une machine à induction alimentée par le stator et le rotor en présence de défauts capteurs." Thesis, Toulouse, INPT, 2010. http://www.theses.fr/2010INPT0107/document.
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Le développement de commandes en boucle fermée pour des entraînements électriques nécessite l'installation de capteurs pour avoir l'information de la rétroaction. Cependant, un éventuel défaut survenant sur l'un des capteurs installés (de courant, de vitesse/position,…) implique un disfonctionnement de la commande conduisant dans la plupart du temps à la mise hors service du système. Ces conséquences sont contraires aux exigences des industriels qui demandent des degrés de fiabilité du système de plus en plus élevés. Des statistiques montrent que le défaut capteur est fréquent. Il est donc impératif de trouver des solutions pour assurer la continuité de service des systèmes électriques dans le cas de présence de ce type de défaut. Tout d'abord, l'étude présentée dans ce manuscrit présente les technologies des différents capteurs installés et ce pour comprendre les raisons et le type de pannes qui pourraient survenir. Ensuite, le système sur lequel la validation des algorithmes développés est décrit. Il s'agit d'un entraînement électrique basé sur une machine à Double Alimentation (MADA) fonctionnant en mode moteur et connectée au réseau via deux convertisseurs. La commande associée est une Commande Directe de Couple (CDC). Elle est validée en mode sain aussi bien par simulation qu'expérimentalement. Après, les études réalisées prennent en considération les défauts capteurs de courants alternatifs et de vitesse/position. Les algorithmes développés, permettant une continuité de service, utilisent une redondance analytique et sont basés sur l'estimation et aussi sur la Détection et l'Isolation d'un éventuel Défaut (DID). Ils sont caractérisés par leur simplicité. Aussi, ils ne sont pas gourmands en termes de consommation en ressources matérielles et leur temps d'exécution est très court. Enfin, la validation expérimentale de ces algorithmes montre bien leur efficacité en cas de défaut, vu que le système s'avère insensible au défaut et continue à fonctionner sans interruption. La commande obtenue est alors tolérante aux défauts capteursThe development of closed loop controls for electrical drives requires the sensor installations in order to get feed back information. Nevertheless, any occurred sensor fault (current sensor,speed/position sensor,…) shows an operation system deterioration which leads in most cases to its shut down. This consequence is in contrast to industrial expectations especially concerning the system high accuracy that they are asking for. Statistic studies point out the sensor faults as frequent. So, it is necessary to find out solutions ensuring the system service continuity in case of any sensor fault. Firstly, the study presented in this work shows the used sensor technologies in order to understand both of the reason and the kind of occurred faults. Secondly, the studied system is presented which is an electrical drive based on a Doubly Fed Induction Machine (DFIM) operating in motor mode and connected to the grid by two inverters. The control developed is a Direct Torque Control (DTC). The control validation, in healthy operating mode, is realised throw simulation and experimentally. After, a study considering alternative current sensor and speed/position sensor faults are achieved. The developed algorithms are based on signal estimation, on a Fault Detection Isolation (FDI) and reconfiguration algorithms. In fact, they are simple to carry out, they don't need much hardware resources for implementation and their execution time is short. Finally, the experimental validation of the developed algorithms shows their efficiency. The system continues working even in presence of a sensor fault. Thus, the obtained control becomes a fault tolerant control thanks to these algorithms
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Chiarello, Andre Garcia. "Detecção e localização de falhas em sistemas mecanicos estacionarios atraves de funções de correlação." [s.n.], 1998. http://repositorio.unicamp.br/jspui/handle/REPOSIP/265204.
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Orientador: Robson PederivaTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecanica
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Resumo: Este trabalho aborda o problema de detecção e localização de falhas em sistemas mecânicos através de modelos matemáticos. Considera-se que o sistema mecânico seja representado por um modelo dinâmico na forma de variáveis de estado, com parâmetros constantes e entradas estacionárias. Definindo-se, apropriadamente, funções de correlação e explorando as propriedades inerentes aos sistemas estacionários, consegue-se obter funções analíticas envolvendo parâmetros físicos do sistema. Posteriormente, demonstra-se que estas funções analíticas podem ser utilizadas para monitorar parâmetros físicos do modelo, ou ainda, identificar parâmetros físicos relacionados a uma falha no sistema. Duas abordagens diferentes para o problema de localização de falhas são desenvolvidas: uma utiliza a estimação de parâmetros e outra utiliza funções de resíduo. Visando uma aplicação em sistemas reais, foram simulados sistemas mecânicos em diversas condições de operação. Os resultados numéricos comprovaram que a metodologia proposta pode ser utilizada no monitoramento de sistemas mecânicos
Abstract: This work deals with the problem of fault detection and location in mechanical systems through mathematical models. It is considered that the mechanical system is represented in a state space form, with constant parameters and stationary inputs. Some correlation functions are appropriately defined involving physical parameters of the stationary system. It is demonstrated that these correlation functions can be transformed to appropriate analytical form (redundancy equations) for fault detection and isolation purposes. Two different approaches are considered: one based on physical parameters estimation and the other based on residuaIs generation. Three different mechanical systems were numerically simulated with conditions (or restrictions) usually found in practical situations. The results showed that the proposed approach is a promising methodology for monitoring stationary mechanical systems
Doutorado
Projeto Mecanico
Doutor em Engenharia Mecânica
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Song, Ruixiang. "Parameter estimation based fault detection and isolation in electrohydraulic systems." 2002. http://hdl.handle.net/1993/19715.
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Chang, Der-Shing, and 張德賢. "State Estimation and Fault Detection Bases on Hybrid Neural Network." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/40527711031314796304.
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碩士國立臺灣科技大學
化學工程研究所
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The hybrid neural network of Tung et al. (1995) is extended to multivariable systems. Since the architecture of the hybrid network is similar to that of Kalman filter,all states of nonlinear systems can be reconstructed once some measurments are measurable. If all states are measurable, it then becomes the problem of data rectification. Comparison is made between nthe hybrid network and Elman net. The results show that for the trained noise level, the hybrid net is comparable to the Elman net.Hower,the proposed hybrid net shows better adaptability on the noise level changes. If some of the states are unmeasurable, the hybrid network naturally leads to an partial recurrent and partial mixed recurrent plus feedforward architecture.More import retains good state estimation with few output measurements corrupted with measurement noise. The state estimation capability of the hybrid network leads it to be a natural fault detection system. The fault origins can be viewed as unmeasured state and they can be estimated from process measurements.A CSTR example is used to illustrate the effectiveness of the neural fault detector. Simulation results show that the proposed system can defect the fault effectively under single or multiple faults . More importantly, it gives very good tracking as the faults develop.
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Labuschagne, Petrus Jacobus. "Automatic clustering with application to time dependent fault detection in chemical processes." Diss., 2009. http://hdl.handle.net/2263/26092.
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Fault detection and diagnosis presents a big challenge within the petrochemical industry. The annual economic impact of unexpected shutdowns is estimated to be $20 billion. Assistive technologies will help with the effective detection and classification of the faults causing these shutdowns. Clustering analysis presents a form of unsupervised learning which identifies data with similar properties. Various algorithms were used and included hard-partitioning algorithms (K-means and K-medoid) and fuzzy algorithms (Fuzzy C-means, Gustafson-Kessel and Gath-Geva). A novel approach to the clustering problem of time-series data is proposed. It exploits the time dependency of variables (time delays) within a process engineering environment. Before clustering, process lags are identified via signal cross-correlations. From this, a least-squares optimal signal time shift is calculated. Dimensional reduction techniques are used to visualise the data. Various nonlinear dimensional reduction techniques have been proposed in recent years. These techniques have been shown to outperform their linear counterparts on various artificial data sets including the Swiss roll and helix data sets but have not been widely implemented in a process engineering environment. The algorithms that were used included linear PCA and standard Sammon and fuzzy Sammon mappings. Time shifting resulted in better clustering accuracy on a synthetic data set based on than traditional clustering techniques based on quantitative criteria (including Partition Coefficient, Classification Entropy, Partition Index, Separation Index, Dunn’s Index and Alternative Dunn Index). However, the time shifted clustering results of the Tennessee Eastman process were not as good as the non-shifted data. CopyrightDissertation (MEng)--University of Pretoria, 2009.
Chemical Engineering
unrestricted
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Nakhaeinejad, Mohsen. "Fault detection and model-based diagnostics in nonlinear dynamic systems." Thesis, 2010. http://hdl.handle.net/2152/ETD-UT-2010-12-2208.
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Modeling, fault assessment, and diagnostics of rolling element bearings and induction motors were studied. Dynamic model of rolling element bearings with faults were developed using vector bond graphs. The model incorporates gyroscopic and centrifugal effects, contact deflections and forces, contact slip and separations, and localized faults. Dents and pits on inner race, outer race and balls were modeled through surface profile changes. Experiments with healthy and faulty bearings validated the model. Bearing load zones under various radial loads and clearances were simulated. The model was used to study dynamics of faulty bearings. Effects of type, size and shape of faults on the vibration response and on dynamics of contacts in presence of localized faults were studied.
A signal processing algorithm, called feature plot, based on variable window averaging and time feature extraction was proposed for diagnostics of rolling element bearings. Conducting experiments, faults such as dents, pits, and rough surfaces on inner race, balls, and outer race were detected and isolated using the feature plot technique. Time features such as shape factor, skewness, Kurtosis, peak value, crest factor, impulse factor and mean absolute deviation were used in feature plots. Performance of feature plots in bearing fault detection when finite numbers of samples are available was shown. Results suggest that the feature plot technique can detect and isolate localized faults and rough surface defects in rolling element bearings. The proposed diagnostic algorithm has the potential for other applications such as gearbox.
A model-based diagnostic framework consisting of modeling, non-linear observability analysis, and parameter tuning was developed for three-phase induction motors. A bond graph model was developed and verified with experiments. Nonlinear observability based on Lie derivatives identified the most observable configuration of sensors and parameters. Continuous-discrete Extended Kalman Filter (EKF) technique was used for parameter tuning to detect stator and rotor faults, bearing friction, and mechanical loads from currents and speed signals. A dynamic process noise technique based on the validation index was implemented for EKF. Complex step Jacobian technique improved computational performance of EKF and observability analysis. Results suggest that motor faults, bearing rotational friction, and mechanical load of induction motors can be detected using model-based diagnostics as long as the configuration of sensors and parameters is observable.text
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Serpas, Mitchell Roy. "Soft Sensors for Process Monitoring of Complex Processes." Thesis, 2012. http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11639.
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Soft sensors are an essential component of process systems engineering schemes. While soft sensor design research is important, investigation into the relationships between soft sensors and other areas of advanced monitoring and control is crucial as well. This dissertation presents two new techniques that enhance the performance of fault detection and sensor network design by integration with soft sensor technology. In addition, a chapter is devoted to the investigation of the proper implementation of one of the most often used soft sensors. The performance advantages of these techniques are illustrated with several cases studies.
First, a new approach for fault detection which involves soft sensors for process monitoring is developed. The methodology presented here deals directly with the state estimates that need to be monitored. The advantage of such an approach is that the nonlinear effect of abnormal process conditions on the state variables can be directly observed. The presented technique involves a general framework for using soft sensor design and computation of the statistics that represent normal operating conditions.
Second, a method for determining the optimal placement of multiple sensors for processes described by a class of nonlinear dynamic systems is described. This approach is based upon maximizing a criterion, i.e., the determinant, applied to the empirical observability gramian in order to optimize certain properties of the process state estimates. The determinant directly accounts for redundancy of information, however, the resulting optimization problem is nontrivial to solve as it is a mixed integer nonlinear programming problem. This paper also presents a decomposition of the optimization problem such that the formulated sensor placement problem can be solved quickly and accurately on a desktop PC.
Many comparative studies, often based upon simulation results, between Extended Kalman filters (EKF) and other estimation methodologies such as Moving Horizon Estimation or Unscented Kalman Filter have been published over the last few years. However, the results returned by the EKF are affected by the algorithm used for its implementation and some implementations may lead to inaccurate results. In order to address this point, this work provides a comparison of several different algorithms for implementation.
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Santos, Inês Vilela dos. "Towards a predictive maintenance methodology of hydraulic pumps." Master's thesis, 2021. http://hdl.handle.net/10773/31362.
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Hydraulic pumps, essential elements in water supply systems, are mainly responsible
for the high energy consumption associated with these systems. It is, therefore, relevant to keep the pumps running in their best possible conditions in order to avoid
further consumption and costs, and also to anticipate possible pump failures. The
best strategy to anticipate the occurrence of failures is to implement preventive and
predictive maintenance plans, instead of corrective maintenance that is still widely
applied. Thus, with the goal of developing a predictive maintenance methodology
applied to hydraulic pumps, this dissertation aims to explore and investigate the
applicability of two techniques that can be integrated into a maintenance plan: the
detection and classification of failures and the estimation of the remaining useful
life (RUL) of the pump. To implement the proposed tasks, simulated data and
measured data from real systems were used, taken from online data repositories,
with values recorded by sensors and with the identified condition of the system.
The first technique allowed, through sensor data with the respectively identified
faults, to train classification algorithms able to identify failures. In the first of
the evaluated case studies, the best of the implemented algorithms identified the
failures associated with the pump data with an accuracy of 82.9%, whereas, in
the second of the evaluated case studies, the algorithm that presented the best
performance obtained an accuracy of 94.6% in identifying the failure mode associated with the pump. The decision tree and ensemble trees algorithms proved
to be the most suitable for the studied purpose. The second technique allowed
to estimate RUL values from sensor data recorded from normal operation to system failure. Although the first RUL implemented case study was an engine, the
second case study was a water pump. The methodology of the RUL model proved
to be relevant because it managed, even with some deviations from the true values, to estimate acceptable values of RUL. An economic analysis was also carried
out, highlighting the relevance of applying RUL estimation models in predictive
maintenance methodologies for hydraulic pumpsAs bombas hidráulicas, elementos essenciais nos sistemas de abastecimento de água, são os principais responsáveis pelos elevados consumos energéticos associados a estes sistemas. Torna-se, portanto, relevante manter as bombas a funcionar nas suas melhores condições possíveis de forma a evitar mais consumos e custos, e também antecipar possíveis falhas nas bombas. A melhor estratégia para antecipar o acontecimento de falhas passa pela implementação de planos de manutenção preventivos e preditivos, ao invés da manutenção corretiva que é ainda muito aplicada. Assim, com vista ao desenvolvimento de uma metodologia de manutenção preditiva aplicada às bombas hidráulicas, esta dissertação tem como objetivo a exploração e investigação da aplicabilidade de duas técnicas que podem ser integradas num plano de manutenção: a deteção e classificação de falhas e a estimativa do tempo de vida útil restante (RUL) de uma bomba. Para implementar as tarefas propostas utilizaram-se dados simulados e dados medidos a partir de sistemas reais, retirados de repositórios de dados online, com valores registados por sensores e com a condição do sistema identificada. A primeira técnica permitiu, através de dados de sensores com as respetivas falhas identificadas, treinar algoritmos de classificação capazes de identificar falhas. No primeiro dos casos de estudo avaliados, o melhor dos algoritmos implementados identificou as falhas associadas aos dados da bomba com uma classificação de desempenho de 82.9%, ao passo que, no segundo dos casos de estudo avaliados, o algoritmo que apresentou melhor desempenho obteve uma classificação de 94.6% na identificação do modo de falha associado à bomba. Os algoritmos de decision trees e ensemble trees demonstraram ser os mais indicados para o propósito estudado. A segunda técnica permitiu calcular previsões de valores do RUL a partir de dados de sensores registados desde uma operação normal até à falha do sistema. Apesar de o primeiro caso de estudo de implementação de RUL ter sido um motor, o segundo caso de estudo foi uma bomba de água. A metodologia do modelo de RUL demonstrou ser pertinente pois conseguiu, ainda que com alguns desvios em relação aos verdadeiros valores, estimar valores aceitáveis de RUL. Elaborou-se ainda uma análise económica que evidencia a relevância em aplicar modelos de cálculo de RUL em metodologias de manutenção preditiva de bombas hidráulicas
Mestrado em Engenharia Mecânica
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Suryawan, Fajar. "Constrained trajectory generation and fault tolerant control based on differential flatness and B-splines." Thesis, 2011. http://hdl.handle.net/1959.13/927247.
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Research Doctorate - Doctor of Philosophy (PhD)This thesis provides a unified treatment of the notions of differential flatness, for the characterisation of continuous-time linear systems, and B-splines, a mathematical concept commonly used in computer graphics. Differential flatness is a property of some controlled (linear or nonlinear) dynamical systems, often encountered in applications, which allows for a complete parameterisation of all system variables (inputs and states) in terms of a finite number of variables, called flat outputs, and a finite number of their time derivatives. The notion of differential flatness for a system is especially useful in situations when explicit trajectory generation is required. In fact, under the differential flatness formalism the motion planning problem, as far as the differential equation is concerned, is trivialised. However, a very important limitation, ubiquitous in all practical applications, is the presence of constraints. The problem of constrained trajectory generation is intimately related to that of optimal control, where one wants to achieve certain objectives with limited resources, and time-optimal control, in which one seeks to perform a task as fast as possible while, at the same time, satisfying all system constraints. In the literature, trajectory generation and [time-] optimal control often use some parameterisation to represent the system's signals. Polynomials and B-splines are a natural choice since they have several desirable properties. However, there has not been much work exploiting the combined properties of differential flatness for linear systems and B-splines. The first focus of this thesis is, hence, to investigate the use of B-splines for constrained trajectory generation of continuous-time linear flat systems in such a way that their respective properties are jointly exploited and complemented. This synthesis offers new methods and insights to the fields of constrained trajectory optimisation, optimal control, and minimum-time trajectory generation. The differential flatness parameterisation also offers analytical redundancy relations. That is, the value of some variables can be algebraically inferred from some other measured variables. This fact can be used to perform algebraic estimation and fault detection in linear and nonlinear systems. The second focus of this thesis is, thus, to develop a method to perform algebraic estimation and fault detection, based structurally on the differential flatness notion, for linear and nonlinear systems, and using a numerical method based on B-splines. The methodology to tackle the focal problems of constrained trajectory generation and fault tolerant control, based on differential flatness and B-splines, is primarily developed for linear systems. Then, experimental validations of the methods, using a laboratory-scale magnetic levitation system, are provided. Finally, some extensions of the ideas to nonlinear systems are discussed.
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(9754367), Pallavi Madhav Kulkarni. "Contributions to Autonomous Operation of a Deep Space Vehicle Power System." Thesis, 2020.
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The electric power system of a deep space vehicle is mission-critical, and needs to operate autonomously because of high latency in communicating with ground-based mission control. Key tasks to be automated include managing loads under various physical constraints, continuously monitoring the system state to detect and locate faults, and efficiently responding to those faults.
This work focuses on three aspects for achieving autonomous, fault-tolerant operation in the dc power system of a spacecraft. First, a sequential procedure is proposed to estimate the node voltages and branch currents in the power system from erroneous sensor measurements. An optimal design for the sensor network is also put forth to enable reliable sensor fault detection and identification. Secondly, a machine-learning based approach that utilizes power-spectrum based features of the current signal is suggested to identify component faults in power electronic converters in the system. Finally, an optimization algorithm is set
forth that decides how to operate the power system under both normal and faulted conditions. Operational decisions include shedding loads, switching lines, and controlling battery charging. Results of case studies considering various faults in the system are presented.
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Lukic, Zdravko. "Design and Practical Implementation of Advanced Reconfigurable Digital Controllers for Low-power Multi-phase DC-DC Converters." Thesis, 2012. http://hdl.handle.net/1807/33855.
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The main goal of this thesis is to develop practical digital controller architectures for multi-phase dc-dc converters utilized in low power (up to few hundred watts) and cost-sensitive applications. The proposed controllers are suitable for on-chip integration while being capable of providing advanced features, such as dynamic efficiency optimization, inductor current estimation, converter component identification, as well as combined dynamic current sharing and fast transient response.
The first part of this thesis addresses challenges related to the practical implementation of digital controllers for low-power multi-phase dc-dc converters. As a possible solution, a multi-use high-frequency digital PWM controller IC that can regulate up to four switching converters (either interleaved or standalone) is presented. Due to its configurability, low current consumption (90.25 μA/MHz per phase), fault-tolerant work, and ability to operate at high switching frequencies (programmable, up to 10 MHz), the IC is suitable to control various dc-dc converters. The applications range from dc-dc converters used in miniature battery-powered electronic devices consuming a fraction of watt to multi-phase dedicated supplies for communication systems, consuming hundreds of watts.
A controller for multi-phase converters with unequal current sharing is introduced and an efficiency optimization method based on logarithmic current sharing is proposed in the second part. By forcing converters to operate at their peak efficiencies and dynamically adjusting the number of active converter phases based on the output load current, a significant improvement in efficiency over the full range of operation is obtained (up to 25%). The stability and inductor current transition problems related to this mode of operation are also resolved.
At last, two reconfigurable digital controller architectures with multi-parameter estimation are introduced. Both controllers eliminate the need for external analog current/temperature sensing circuits by accurately estimating phase inductor currents and identifying critical phase parameters such as equivalent resistances, inductances and output capacitance. A sensorless non-linear, average current-mode controller is introduced to provide fast transient response (under 5 μs), small voltage deviation and dynamic current sharing with multi-phase converters. To equalize the thermal stress of phase components, a conduction loss-based current sharing scheme is proposed and implemented.