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Rozprawy doktorskie na temat „Fault identification and classification”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Venkatesh, Vidya. "Fault Classification and Location Identification on Electrical Transmission Network Based on Machine Learning Methods." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5582.

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Power transmission network is the most important link in the country’s energy system as they carry large amounts of power at high voltages from generators to substations. Modern power system is a complex network and requires high-speed, precise, and reliable protective system. Faults in power system are unavoidable and overhead transmission line faults are generally higher compare to other major components. They not only affect the reliability of the system but also cause widespread impact on the end users. Additionally, the complexity of protecting transmission line configurations increases w

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Li, Zhongliang. "Data-driven fault diagnosis for PEMFC systems." Thesis, Aix-Marseille, 2014. http://www.theses.fr/2014AIXM4335/document.

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Cette thèse est consacrée à l'étude de diagnostic de pannes pour les systèmes pile à combustible de type PEMFC. Le but est d'améliorer la fiabilité et la durabilité de la membrane électrolyte polymère afin de promouvoir la commercialisation de la technologie des piles à combustible. Les approches explorées dans cette thèse sont celles du diagnostic guidé par les données. Les techniques basées sur la reconnaissance de forme sont les plus utilisées. Dans ce travail, les variables considérées sont les tensions des cellules. Les résultats établis dans le cadre de la thèse peuvent être regroupés en

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Romano, Donato. "Machine Learning algorithms for predictive diagnostics applied to automatic machines." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22319/.

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In questo lavoro di tesi è stato analizzato l'avvento dell'industria 4.0 all'interno dell' industria nel settore packaging. In particolare, è stata discussa l'importanza della diagnostica predittiva e sono stati analizzati e testati diversi approcci per la determinazione di modelli descrittivi del problema a partire dai dati. Inoltre, sono state applicate le principali tecniche di Machine Learning in modo da classificare i dati analizzati nelle varie classi di appartenenza.

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Syal, Manan. "Untestable Fault Identification Using Implications." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/46173.

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Untestable faults in circuits are defects/faults for which there exists no test pattern that can either excite the fault or propagate the fault effect to an observable point, which could be either a Primary output (PO) or a scan flip-flop. The current state-of-the-art automatic test pattern generators (ATPGs) spend a lot of time in trying to generate a test sequence for the detection of untestable faults, before aborting on them, or identifying them as untestable, given enough time. Thus, it would be beneficial to quickly identify faults that are redundant/untestable, so that tools such as ATP

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Waly, Hashem. "Automated Fault Identification - Kernel Trace Analysis." Thesis, Université Laval, 2011. http://www.theses.ulaval.ca/2011/28246/28246.pdf.

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Fan, Wen. "ADVANCED FAULT AREA IDENTIFICATION AND FAULT LOCATION FOR TRANSMISSION AND DISTRIBUTION SYSTEMS." UKnowledge, 2019. https://uknowledge.uky.edu/ece_etds/144.

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Fault location reveals the exact information needed for utility crews to timely and promptly perform maintenance and system restoration. Therefore, accurate fault location is a key function in reducing outage time and enhancing power system reliability. Modern power systems are witnessing a trend of integrating more distributed generations (DG) into the grid. DG power outputs may be intermittent and can no longer be treated as constants in fault location method development. DG modeling is also difficult for fault location purpose. Moreover, most existing fault location methods are not applicab

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7

Felldin, Markus. "Machine Learning Methods for Fault Classification." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-183132.

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This project, conducted at Ericsson AB, investigates the feasibility of implementing machine learning techniques in order to classify dump files for more effi cient trouble report routing. The project focuses on supervised machine learning methods and in particular Bayesian statistics. It shows that a program utilizing Bayesian methods can achieve well above random prediction accuracy. It is therefore concluded that machine learning methods may indeed become a viable alternative to human classification of trouble reports in the near future.<br>Detta examensarbete, utfört på Ericsson AB, ämnar

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8

Mufti, Muid Ur-Rahman. "Fault detection and identification using fuzzy wavelets." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/16472.

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Wang, Tsang-Yi Varshney Pramod K. "Distributed fault-tolerant classification using coding theory." Related Electronic Resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2003. http://wwwlib.umi.com/cr/syr/main.

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Covella, Vito Vincenzo. "Multi-node Fault Classification using Machine Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22867/.

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An HPC system, a system with much more computational power than general computing systems, is a complex system made up of different sections and many computing nodes. In such systems failures can arise for different reasons: because of the interactions among the components, because of the specific technologies used or because of bugs in the software. In order to reach Exascale performances and guarantee availability and reliability it is important to detect and recover from these anomalies. In this thesis we propose a fault classification method based on machine learning. Other researchers

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Duatis, Juárez Jordi. "FAST : a fault detection and identification software tool." Doctoral thesis, Universitat Politècnica de Catalunya, 2016. http://hdl.handle.net/10803/386567.

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The aim of this work is to improve the reliability and safety of complex critical control systems by contributing to the systematic application of fault diagnosis. In order to ease the utilization of fault detection and isolation (FDI) tools in the industry, a systematic approach is required to allow the process engineers to analyze a system from this perspective. In this way, it should be possible to analyze this system to find if it provides the required fault diagnosis and redundancy according to the process criticality. In addition, it should be possible to evaluate what-if scenarios by sl

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12

El-Gamal, Mohamed A. "Fault location and parameter identification in analog circuits." Ohio : Ohio University, 1990. http://www.ohiolink.edu/etd/view.cgi?ohiou1172776742.

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13

Luo, Dapeng. "SYSTEM IDENTIFICATION AND FAULT DETECTION OF COMPLEX SYSTEMS." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3583.

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The proposed research is devoted to devising system identification and fault detection approaches and algorithms for a system characterized by nonlinear dynamics. Mathematical models of dynamical systems and fault models are built based on observed data from systems. In particular, we will focus on statistical subspace instrumental variable methods which allow the consideration of an appealing mathematical model in many control applications consisting of a nonlinear feedback system with nonlinearities at both inputs and outputs. Different solutions within the proposed framework are presented t

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14

Marwala, Tshilidzi. "Fault identification using neural networks and vibration data." Thesis, University of Cambridge, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.621401.

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15

Suda, Jessica Lynn. "Misfire-Fault Classification for Future On-Board Diagnostics III Vehicles." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/theses/2372.

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Current OBD-II vehicle systems detect misfires by monitoring slight variances of crankshaft acceleration throughout power-strokes of each of the engine’s cylinders. If the PCM determines that the acceleration of the engine’s crankshaft is inappropriate, it concludes a misfire is detected. However, after this misfire is detected, the technician still needs to diagnose (isolate) the root-cause. Diagnosis is no easy task, especially with several potential subsystems that could be at fault: fuel injection, air-intake, sparkignition, and engine-mechanical. With this being said, it is difficult for

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16

Steele, Andrew D. "Time constrained qualitative model-based parameter identification." Thesis, Heriot-Watt University, 1996. http://hdl.handle.net/10399/735.

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17

Henningsson, Kennet. "A Fault Classification Approach to Software Process Improvement." Licentiate thesis, Karlskrona : Blekinge Institute of Technology [Blekinge tekniska högskola], 2005. http://www.bth.se/fou/Forskinfo.nsf/allfirst2/2b9d5998e26ed1b2c12571230047386b?OpenDocument.

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18

Horton, Kirk Gerritt. "FAULT DETECTION AND MODEL IDENTIFICATION IN LINEAR DYNAMICAL SYSTEMS." NCSU, 2001. http://www.lib.ncsu.edu/theses/available/etd-20010403-112605.

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<p>Horton, Kirk Gerritt. Fault Detection and Model Identification in Linear Dynamical Systems. (Under the direction of Dr. Stephen La Vern Campbell.) Linear dynamical systems, Ex'+Fx=f(t), in which E is singular, are useful in a wide variety of applications. Because of this wide spread applicability, much research has been done recently to develop theory for the design of linear dynamical systems. A key aspect of system design is fault detection and isolation (FDI). One avenue of FDI is via the multi-model approach, in which the parameters of the nominal, unfailed model of the system are known

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19

Yoon, Heebyung. "Fault detection and identification techniques for embedded analog circuits." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/13041.

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20

Brun, Yuriy 1981. "Software fault identification via dynamic analysis and machine learning." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/17939.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.<br>Includes bibliographical references (p. 65-67).<br>I propose a technique that identifies program properties that may indicate errors. The technique generates machine learning models of run-time program properties known to expose faults, and applies these models to program properties of user-written code to classify and rank properties that may lead the user to errors. I evaluate an implementation of the technique, the Fault Invariant Classifier, that demonstrates the efficacy

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21

Arunyanart, Pirapat. "Fault Identification In Drivetrain Components Using Vibration Signature Analysis." University of Akron / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=akron1428351973.

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Klein, Stéphane. "Identification of discrete event systems for fault detection purposes." Cachan, Ecole normale supérieure, 2005. http://www.theses.fr/2005DENS0014.

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Accroître la disponibilité des systèmes de production par la réduction de leurs temps d'arrêt est un enjeu économique majeur. La méthode de surveillance à base de modèles où l'évolution réelle du système est comparée à son évolution idéale est la plus appropriée pour détecter au plus tôt les fautes qui peuvent conduire à une défaillance. Cette thèse propose la construction d'un modèle du fonctionnement normal des Systèmes à Evénements Discrets à l'aide d'une technique d'identification. Pour cela, une classe d'automates est définie et un algorithme d'identification paramétrable développé. Pour

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23

Mehranbod, Nasir Soroush Masoud. "A probabilistic approach for sensor fault detection and identification /." Philadelphia : Drexel University, 2002. http://dspace.library.drexel.edu/handle/1721.1/68.

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Guo, Qiang. "Improving fault coverage and minimising the cost of fault identification when testing from finite state machines." Thesis, Brunel University, 2006. http://bura.brunel.ac.uk/handle/2438/3064.

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Software needs to be adequately tested in order to increase the confidence that the system being developed is reliable. However, testing is a complicated and expensive process. Formal specification based models such as finite state machines have been widely used in system modelling and testing. In this PhD thesis, we primarily investigate fault detection and identification when testing from finite state machines. The research in this thesis is mainly comprised of three topics - construction of multiple Unique Input/Output (UIO) sequences using Metaheuristic Optimisation Techniques (MOTs), the

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Buzza, Matthew. "An Evaluation of Classification Algorithms for Machinery Fault Diagnosis." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1490702571145903.

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Baravdish, Ninos. "Information Fusion of Data-Driven Engine Fault Classification from Multiple Algorithms." Thesis, Linköpings universitet, Fordonssystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176508.

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As the automotive industry constantly makes technological progress, higher demands are placed on safety, environmentally friendly and durability. Modern vehicles are headed towards increasingly complex system, in terms of both hardware and software making it important to detect faults in any of the components. Monitoring the engine’s health has traditionally been done using expert knowledge and model-based techniques, where derived models of the system’s nominal state are used to detect any deviations. However, due to increased complexity of the system this approach faces limitations regarding

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Fernández, Canti Rosa Ma. "A Bayesian approach to robust identification: application to fault detection." Doctoral thesis, Universitat Politècnica de Catalunya, 2013. http://hdl.handle.net/10803/116329.

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In the Control Engineering field, the so-called Robust Identification techniques deal with the problem of obtaining not only a nominal model of the plant, but also an estimate of the uncertainty associated to the nominal model. Such model of uncertainty is typically characterized as a region in the parameter space or as an uncertainty band around the frequency response of the nominal model. Uncertainty models have been widely used in the design of robust controllers and, recently, their use in model-based fault detection procedures is increasing. In this later case, consistency between ne

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Zhou, Qian. "Fault identification for aircraft distribution system using harmonic impedance measurement." Thesis, University of Nottingham, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.517650.

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Adams, Peter. "Fault demarcation and electromagnetic interference identification for broadband DSL access." Thesis, Queen Mary, University of London, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.538377.

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Chen, Jie. "Fault Section Identification for Power Distribution Systems Using Online Measurements." UKnowledge, 2015. http://uknowledge.uky.edu/ece_etds/70.

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Fault location is very important for distribution systems, and quickly identifying the fault and restoring the system can help reduce the outage time and make the system more reliable. In this thesis, a method for locating faults on distribution systems is introduced to quickly identify the faulted feeder sections by using the overcurrent information from the switches in the system. Fuzzy logic is utilized. The proposed method can quickly and accurately locate faulted sections with different fault locations, fault types and fault resistances. The method is applicable to cases with single-fault

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Armstrong, Peter Ross 1950. "Model identification with application to building control and fault detection." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28805.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Architecture, 2004.<br>Includes bibliographical references (p. 110-115).<br>(cont.) may still be solved as an unconstrained linear least squares problem. To enforce the constraint on system eigenvalues, the problem is formulated as an unconstrained mixed (linear and non-linear) least-squares problem, which is easier to solve than the corresponding problem with linear objective function and non-linear constraints. The (usually unfounded) assumption on which the normal equations are based--that observations of the independent varia

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Smith, Finlay S. "Combining symbolic conflict recognition with Markov chains for fault identification." Thesis, University of Edinburgh, 2002. http://hdl.handle.net/1842/11417.

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A novel approach is presented in this thesis that exploits uncertain information on the behavioural description of system components to identify possible fault behaviours in physical systems. The result is a diagnostic system that utilises all available evidence at each stage. The approach utilises the standard conflict recognition technique developed in the well-known General Diagnostic Engine framework to support diagnostic inference, through the production of both rewarding and penalising evidence. The penalising evidence is derived from the conflict sets and the rewarding evidence is deriv

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Pietersen, Willem Hermanus. "System identification for fault tolerant control of unmanned aerial vehicles." Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/4164.

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Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2010.<br>ENGLISH ABSTRACT: In this project, system identification is done on the Modular Unmanned Aerial Vehicle (UAV). This is necessary to perform fault detection and isolation, which is part of the Fault Tolerant Control research project at Stellenbosch University. The equations necessary to do system identification are developed. Various methods for system identification is discussed and the regression methods are implemented. It is shown how to accommodate a sudden change in aircraft parameters due

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Schneider, C. "Using unsupervised machine learning for fault identification in virtual machines." Thesis, University of St Andrews, 2015. http://hdl.handle.net/10023/7327.

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Self-healing systems promise operating cost reductions in large-scale computing environments through the automated detection of, and recovery from, faults. However, at present there appears to be little known empirical evidence comparing the different approaches, or demonstrations that such implementations reduce costs. This thesis compares previous and current self-healing approaches before demonstrating a new, unsupervised approach that combines artificial neural networks with performance tests to perform fault identification in an automated fashion, i.e. the correct and accurate determinati

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SHI, Juanjuan. "Morphology-based Fault Feature Extraction and Resampling-free Fault Identification Techniques for Rolling Element Bearing Condition Monitoring." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/33422.

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As the failure of a bearing could cause cascading breakdowns of the mechanical system and then lead to costly repairs and production delays, bearing condition monitoring has received much attention for decades. One of the primary methods for this purpose is based on the analysis of vibration signal measured by accelerometers because such data are information-rich. The vibration signal collected from a defective bearing is, however, a mixture of several signal components including the fault-generated impulses, interferences from other machine components, and background noise, where fault-induce

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Säfdal, Joakim. "Data-Driven Engine Fault Classification and Severity Estimation Using Interpolated Fault Modes from Limited Training Data." Thesis, Linköpings universitet, Fordonssystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-173916.

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Today modern vehicles are expected to be safe, environmentally friendly, durable and economical. Monitoring the health of the vehicle is therefore more important than ever. As the complexity of vehicular systems increases the need for efficient monitoring methods has increased as well. Traditional methods of deriving models for the systems are today not as efficient as the complexity of the systems increases the time and skill needed to implement the models. An alternative is data driven methods where a collection of data associated with the behavior of the system is used to draw conclusions o

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Jonson, Sten. "Identification and forensic classification of amphetamine /." Linköping : Univ, 2000. http://www.bibl.liu.se/liupubl/disp/disp2000/tek641s.htm.

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Lindgren, David. "Projection techniques for classification and identification /." Linköping : Univ, 2004. http://www.bibl.liu.se/liupubl/disp/disp2005/tek915s.pdf.

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Panagiotakopoulos, Georgios. "Statistical Critical Path Identification and Classification." OpenSIUC, 2011. https://opensiuc.lib.siu.edu/theses/573.

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This thesis targets the problem of critical path identification in sub-micron devices. Delays are described using Probability density functions (Pdfs) in order to model the probabilistic nature of the problem. Thus, a deterministic critical path response is not possible. The probability that each path is critical is reported instead. Extensive literature review has being done and presented in detail. Heuristics for accurate critical path calculations are described and results are compared to those from Monte Carlo simulations.

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Rejeb, Sfar Asma. "Classification fine d'objets : identification d'espèces végétales." Thesis, Paris, ENST, 2014. http://www.theses.fr/2014ENST0046/document.

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Nous étudions la problématique de classification dite fine en se concentrant sur la détermination des espèces botaniques à partir d’images de feuilles. Nous nous intéressons aussi bien à la description et la représentation de l’objet qu’aux algorithmes de classification et des scénarios d’identification utiles à l’utilisateur. Nous nous inspirons du processus manuel des botanistes pour introduire une nouvelle représentation hiérarchique des feuilles. Nous proposons aussi un nouveau mécanisme permettant d’attirer l’attention au tour de certains points caractéristiques de l’objet et d’apprendre

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Rejeb, Sfar Asma. "Classification fine d'objets : identification d'espèces végétales." Electronic Thesis or Diss., Paris, ENST, 2014. http://www.theses.fr/2014ENST0046.

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Nous étudions la problématique de classification dite fine en se concentrant sur la détermination des espèces botaniques à partir d’images de feuilles. Nous nous intéressons aussi bien à la description et la représentation de l’objet qu’aux algorithmes de classification et des scénarios d’identification utiles à l’utilisateur. Nous nous inspirons du processus manuel des botanistes pour introduire une nouvelle représentation hiérarchique des feuilles. Nous proposons aussi un nouveau mécanisme permettant d’attirer l’attention au tour de certains points caractéristiques de l’objet et d’apprendre

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Roth, Matthias. "Identification and fault diagnosis of industrial closed-loop discrete event systems." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2010. http://tel.archives-ouvertes.fr/tel-00561906.

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La compétitivité des entreprises manufacturières dépend fortement de la productivité des machines etdes moyens de production. Pour garantir un haut niveau de productivité il est indispensable de minimiser lestemps d'arrêt dus aux fautes ou dysfonctionnements. Cela nécessite des méthodes efficaces pour détecter et isolerles fautes apparues dans un système (FDI). Dans cette thèse, une méthode FDI à base de modèles est proposée.La méthode est conçue pour la classe des systèmes à événements discrets industriels composés d'une bouclefermée du contrôleur et du processus. En comparant les comportemen

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Li, Yongli. "Identification algorithms, fault selectors and tracking of states for digital relays." Doctoral thesis, Universite Libre de Bruxelles, 1993. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/212824.

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Alturas, Ahmed Mohamed. "On the identifiability, parameter identification and fault diagnosis of induction machines." Thesis, University of Newcastle upon Tyne, 2016. http://hdl.handle.net/10443/3232.

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Due to their reliability and low cost, induction machines have been widely utilized in a large variety of industrial applications. Although these machines are rugged and reliable, they are subjected to various stresses that might result in some unavoidable parameter changes and modes of failures. A common practice in induction machine parameter identification and fault diagnosis techniques is to employ a machine model and use the external measurements of voltage, current, speed, and/or torque in model solution. With this approach, it might be possible to get an infinite number of mathematical

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Appel, Jean-Paul. "Online system identification for fault tolerant control of unmanned aerial vehicles." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/80125.

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Thesis (MScEng)--Stellenbosch University, 2013.<br>ENGLISH ABSTRACT: In this thesis the strategy for performing System Identification on an aircraft is presented. The ultimate aim of this document is to outline the steps required for successful aircraft parameter estimation within a Fault Tolerant Control Framework. A brief derivation of the classical 6 degree-of-freedom aircraft model is firstly presented. The derivation gives insight into the aircraft dynamics that are to be used to estimate the aircraft parameters, and provides a basis for the methods provided in this thesis. Differen

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Alamyal, Mohamoud Omran A. "Evaluation of stochastic optimisation algorithms for induction machine winding fault identification." Thesis, University of Newcastle upon Tyne, 2013. http://hdl.handle.net/10443/1937.

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This thesis is concerned with parameters identification and winding fault detection in induction motors using three different stochastic optimisation algorithms, namely genetic algorithm (GA), tabu search (TS) and simulated annealing (SA). Although induction motors are highly reliable, require low maintenance and have relatively high efficiency, they are subject to many electrical and mechanical types of faults. Undetected faults can lead to serious machine failures. Fault identification is, therefore, essential in order to detect and diagnose potential failures in electrical motors. Conventio

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GHOSH, SWAROOP. "SCAN CHAIN FAULT IDENTIFICATION USING WEIGHT-BASED CODES FOR SoC CIRCUITS." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1085765670.

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Harichandran, Aparna. "Sensor Placement, Operation Identification and Fault Detection for Automated Construction Monitoring." Thesis, Curtin University, 2022. http://hdl.handle.net/20.500.11937/87927.

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Michael, Inies Raphael Chemmannoor. "FAULT LINKS: IDENTIFYING MODULE AND FAULT TYPES AND THEIR RELATIONSHIP." Lexington, Ky. : [University of Kentucky Libraries], 2004. http://lib.uky.edu/ETD/ukycosc2004t00211/MasterDegreeThesisReportIniesRCM.pdf.

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Thesis (m.s.)--University of Kentucky, 2004.<br>Title from document title page (viewed Jan. 7, 2005). Document formatted into pages; contains vii, p. 103 : ill. Includes abstract and vita. Includes bibliographical references (p. 99-101).

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Chun, Jongsik. "Computer assisted classification and identification of actinomycetes." Thesis, University of Newcastle Upon Tyne, 1995. http://hdl.handle.net/10443/410.

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Three computer software packages were written in the C++ language for the analysis of numerical phenetic, 16S rRNA sequence and pyrolysis mass spectrometric data. The X program, which provides routines for editing binary data, for calculating test error, for estimating cluster overlap and for selecting diagnostic and selective tests, was evaluated using phenotypic data held on streptomycetes. The AL16S program has routines for editing 16S rRNA sequences, for determining secondary structure, for finding signature nucleotides and for comparative sequence analysis; it was used to analyse 16S rRNA

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