Littérature scientifique sur le sujet « Network fault model »
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Articles de revues sur le sujet "Network fault model"
Bae, Jangsik, Meonghun Lee et Changsun Shin. « A Data-Based Fault-Detection Model for Wireless Sensor Networks ». Sustainability 11, no 21 (5 novembre 2019) : 6171. http://dx.doi.org/10.3390/su11216171.
Texte intégralHan, Bing, Xiaohui Yang, Yafeng Ren et Wanggui Lan. « Comparisons of different deep learning-based methods on fault diagnosis for geared system ». International Journal of Distributed Sensor Networks 15, no 11 (novembre 2019) : 155014771988816. http://dx.doi.org/10.1177/1550147719888169.
Texte intégralShadi, Mohammad Reza, Hamid Mirshekali, Rahman Dashti, Mohammad-Taghi Ameli et Hamid Reza Shaker. « A Parameter-Free Approach for Fault Section Detection on Distribution Networks Employing Gated Recurrent Unit ». Energies 14, no 19 (5 octobre 2021) : 6361. http://dx.doi.org/10.3390/en14196361.
Texte intégralLi, Zhi Chun. « A Simple SOM Neural Network Based Fault Detection Model for Fault Diagnosis of Rolling Bearings ». Applied Mechanics and Materials 397-400 (septembre 2013) : 1321–25. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.1321.
Texte intégralWang, Zhenxing, Haijun Zhang, Huayang Wang, Zhijun Bi, Xiujing He, Qi Wang et Xiangzong Yu. « Analysis of modeling and fault line selection method for Single-phase Intermittent fault of distribution network ». Journal of Physics : Conference Series 2355, no 1 (1 octobre 2022) : 012047. http://dx.doi.org/10.1088/1742-6596/2355/1/012047.
Texte intégralShakya, Subarna. « Pollination Inspired Clustering Model for Wireless Sensor Network Optimization ». September 2021 3, no 3 (29 novembre 2021) : 196–207. http://dx.doi.org/10.36548/jsws.2021.3.006.
Texte intégralNai-Quan Su, Nai-Quan Su, Qing-Hua Zhang Nai-Quan Su, Shao-Lin Hu Qing-Hua Zhang, Xiao-Xiao Chang Shao-Lin Hu et Mei-Chao Chen Xiao-Xiao Chang. « Petrochemical Gearbox Fault Location and Diagnosis Method Based on Distributed Bayesian Model and Neural Network ». 電腦學刊 33, no 3 (juin 2022) : 159–69. http://dx.doi.org/10.53106/199115992022063303013.
Texte intégralPatan, Krzysztof, et Józef Korbicz. « Nonlinear model predictive control of a boiler unit : A fault tolerant control study ». International Journal of Applied Mathematics and Computer Science 22, no 1 (1 mars 2012) : 225–37. http://dx.doi.org/10.2478/v10006-012-0017-6.
Texte intégralBasnet, Barun, Hyunjun Chun et Junho Bang. « An Intelligent Fault Detection Model for Fault Detection in Photovoltaic Systems ». Journal of Sensors 2020 (9 juin 2020) : 1–11. http://dx.doi.org/10.1155/2020/6960328.
Texte intégralZhang, Wubing. « Data Mining Technology for Equipment Machinery and Information Network Data Resources ». Security and Communication Networks 2022 (3 août 2022) : 1–8. http://dx.doi.org/10.1155/2022/5928611.
Texte intégralThèses sur le sujet "Network fault model"
Ozkok, Ozlem. « A realistic model of network survivability ». Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03sep%5FOzkok.pdf.
Texte intégralThesis advisor(s): Geoffrey Xie, Alex Bordetsky. Includes bibliographical references (p. 47-48). Also available online.
Cabezas, Rodríguez Juan Pablo. « Generative adversarial network based model for multi-domain fault diagnosis ». Tesis, Universidad de Chile, 2019. http://repositorio.uchile.cl/handle/2250/170996.
Texte intégralCon el uso de las redes neuronal profundas ganando terreno en el área de PHM, los sensores disminuyendo progresivamente su precio y mejores algoritmos, la falta de datos se ha vuelto un problema principal para los modelos enfocados en datos. Los datos etiquetados y aplicables a escenarios específicos son, en el mejor de los casos, escasos. El objetivo de este trabajo es desarrollar un método para diagnosticas el estado de un rodamiento en situaciones con datos limitados. Hoy en día la mayoría de las técnicas se enfocan en mejorar la precisión del diagnóstico y en estimar la vida útil remanente en componentes bien documentados. En el presente, los métodos actuales son ineficiente en escenarios con datos limitados. Se desarrolló un método en el cual las señales vibratorias son usadas para crear escalogramas y espectrogramas, los cuales a su vez se usan para entrenar redes neuronales generativas y de clasificación, en función de diagnosticar un set de datos parcial o totalmente desconocido, en base a uno conocido. Los resultados se comparan con un método más sencillo en el cual la red para clasificación es entrenada con el set de datos conocidos y usada directamente para diagnosticar el set de datos desconocido. El Case Western Reserve University Bearing Dataset y el Machine Failure Prevention Technology Bearing Dataset fueron usados como datos de entrada. Ambos sets se usaron como conocidos tanto como desconocidos. Para la clasificación una red neuronal convolucional (CNN por sus siglas en inglés) fue diseñada. Una red adversaria generativa (GAN por sus siglas en inglés) fue usada como red generativa. Esta red fue basada en una introducida en el paper StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation. Los resultados fueron favorables para la red CNN mientras que fueron -en general- desfavorables para la red GAN. El análisis de resultados sugiere que la función de costo es inapropiada para el problema propuesto. Las conclusiones dictaminan que la traducción imagen-a-imagen basada en la función ciclo no funciona correctamente en señal vibratorias para diagnóstico de rodamientos. With the use of deep neural networks gaining notoriety on the prognostics & health management field, sensors getting progressively cheaper and improved algorithms, the lack of data has become a major issue for data-driven models. Data which is labelled and applicable for specific scenarios is scarce at best. The purpose of this works is to develop a method to diagnose the health state of a bearing on limited data situations. Now a days most techniques focus on improving accuracy for diagnosis and estimating remaining useful life on well documented components. As it stands, current methods are ineffective on limited data scenarios. A method was developed were in vibration signals are used to create scalograms and spectrograms, which in turn are used to train generative and classification neural networks with the goal of diagnosing a partially or totally unknown dataset based on a fully labelled one. Results were compared to a simpler method in which a classification network is trained on the labelled dataset to diagnose the unknown dataset. As inputs the Case Western Reserve University Bearing Dataset (CWR) and the Society for Machine Failure Prevention Technology Bearing Dataset. Both datasets are used as labelled and unknown. For classification a Convolutional Neural Network (CNN) is designed. A Generative Adversarial Network (GAN) is used as generative model. The generative model is based of a previous paper called StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation. Results were favourable for the CNN network whilst generally negative for the GAN network. Result analysis suggests that the cost function is unsuitable for the proposed problem. Conclusions state that cycle based image-to-image translation does not work correctly on vibration signals for bearing diagnosis.
Fani, Mehran. « Fault diagnosis of an automotive suspension system ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016.
Trouver le texte intégralAull, Mark J. « Comparison of Fault Detection Strategies on a Low Bypass Turbofan Engine Model ». University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1321368833.
Texte intégralAlmulla, Muhannad. « Implementation of an Arc Model for MV Network with Resonance Earthing ». Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278499.
Texte intégralDen vanligaste feltypen i elektriska kraftsystem är fas till jord. I denna typ avfel utvecklas vanligtvis en elektrisk ljusbåge. Examensarbetet presenterar enmatematisk modell som beskriver ljusbågens beteende under ett fel. Bågmodellenhar verifierats baserat på verkliga tester och simuleringar som utfördespå ett system som har resonansjordningsspole.Dessutom har två studier genomförts på samma verifierade system. Denförsta studien genomfördes för att se effekten av avstämning av den resonantajordningspolen på olika nivåer. Det noterades att avstämning av spolen påverkadeACoch DC-komponenterna i ljusbågsfel.Avstämningen påverkade ocksåljusbågens släckning.Den andra studien har tittat på effekterna av att implementera ett parallelltmotstånd till den resonanta jordningsspolen. Testen har utförts med olikainställda värden på motståndet. I några av de studerade fallen och under testperiodenhar motståndet påverkat ljusbågens självsläckande beteende.
Chen, Yun. « Mining Dynamic Recurrences in Nonlinear and Nonstationary Systems for Feature Extraction, Process Monitoring and Fault Diagnosis ». Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6072.
Texte intégralChauvin, Benjamin. « Applicability of the mechanics-based restoration : boundary conditions, fault network and comparison with a geometrical method ». Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0160/document.
Texte intégralStructural restoration aims to recover rock paleo-geometries and to validate structural interpretations. The classical methods are based on geometric/kinematic assumptions and impose a style of deformation. Geomechanical methods, by integrating rock elastic behavior and fundamental mechanical conservation laws, aim to solve issues of classical methods. However several studies show that the geomechanical restoration lacks physical consistency in particular because of the boundary conditions. There are uncertainties on the choice of the elastic properties, and the meshing constraints limit this method to be used as a validation tool of structural interpretations. The choice of a specific restoration method is difficult because there are many geomechanical restoration approaches, in addition to the numerous geometric/kinematic methods. Firstly, this thesis presents a review of the various 3D geomechanical methods to unfold and unfault a 3D geological model. The objective is to present their, theoretical and practical, strengths and limits. Secondly, through the restoration of a structural sandbox model, we worked on the choice of adequate boundary conditions to get a proper restored model. This structural sandbox model was deformed in laboratory and presents several analogies with supra-salt extensional structures. Thanks to the observation of the analog model geometry through time on a cross section, we show that a lateral shortening boundary condition is necessary. We show that this shortening can be estimated by the area-depth method. Moreover we define new fault contact conditions to handle complex fault networks. These novel conditions tie internal fault borders and join parts of offset faults. Thirdly, the test of several elastic parameters shows that Young’s modulus, homogeneous within a geological model, has almost no effect on the restoration displacement field. However, Poisson’s ratio has a significant impact on the volume dilatation. Finally, we compare the mechanics-based restoration method with a geometric-based method relying on a chronostratigraphic model (GeoChron) mapping any point of the subsurface to its image in depositional (Wheeler) space. We show that both methods provide a geometrically similar restored state for the analog model. The geometric method has numerous advantages to quickly and accurately get a restored model, but it lacks flexibility on the choice of the deformation constraints. The geomechanical restoration method force is to define custom boundary conditions and specific mechanical behaviors to handle complex contexts
Lanciotti, Noemi. « Amélioration de la robustesse des machines synchrones spéciales multi phases dans un contexte de transport urbain ». Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLN055/document.
Texte intégralFive-phase flux switching machines have a fault tolerance and robustness that makes them very interesting from the point of view of reliability, as shown in chapter one of this work. In our studies we have explored the possibility of detecting faults that affect this type of machine using the signature of stator vibrations.Using the physical and mathematical tools presented in chapter two, we improved two multyphisics models, one based on finite elements method that it's presented in chapter three and the seconde one analitycal model, called permeance networks, in chapter four. The vibratory behavior of the machine was studied using these two models, under healthy and faulty conditions, in order to know how this behavior is influenced by the electrical and magnetic magnitudes of the machine. In addition, we have studied the possibility of detecting and discriminating different types of faults. Analytical model is a good estimator of fault behavior of the machine, despite its differences with the simulation.In chapter five, the two multiphysical models have been validated by experimental tests and we have been able to explain fault behavior by mechanical origin rather than magnetic origin.Finally, in chapter six, we used both models to study the fault behavior of the machine, at speeds above the experimental limit (3100 rpm)
Pospíšil, Zdeněk. « Indikace zemních spojení na venkovních vedeních ». Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-264926.
Texte intégralChua, Eng Hong. « Determine network survivability using heuristic models ». Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Mar%5FChua%5FEngHong.pdf.
Texte intégralLivres sur le sujet "Network fault model"
Kumar, G. Prem. ATM network fault management using realistic abductive reasoning. Bangalore : Dept. of Electrical Communication Engineering, Indian Institute of Science, 1997.
Trouver le texte intégralR, Callahan John, Whetten Brian et United States. National Aeronautics and Space Administration., dir. Specification and design of a fault recovery model for the reliable multicast protocol. [Washington, DC] : National Aeronautics and Space Administration, 1996.
Trouver le texte intégralDing, Steven X. Model-Based Fault Diagnosis Techniques : Design Schemes, Algorithms and Tools. 2e éd. London : Springer London, 2013.
Trouver le texte intégralButterfield, A. Memory models : A formal analysis using VDM. Dublin : Trinity College, Department of Computer Science, 1992.
Trouver le texte intégralL, Montgomery Todd, Whetten Brian et United States. National Aeronautics and Space Administration., dir. Fault recovery in the Reliable Multicast Protocol. [Washington, D.C.] : National Aeronautics and Space Administration, 1995.
Trouver le texte intégralDing, Steven X. Model-Based Fault Diagnosis Techniques. Springer, 2008.
Trouver le texte intégralBayesian Networks in Fault Diagnosis : Practice and Application. WSPC, 2018.
Trouver le texte intégralModel-Based Fault Diagnosis Techniques : Design Schemes, Algorithms and Tools. Springer London, Limited, 2015.
Trouver le texte intégralDing, Steven X. Model-Based Fault Diagnosis Techniques : Design Schemes, Algorithms and Tools. Springer, 2013.
Trouver le texte intégralWalter, Eric, et Richard Walter. Data Acquisition from Light-Duty Vehicles Using OBD and CAN. SAE International, 2018. http://dx.doi.org/10.4271/r-458.
Texte intégralChapitres de livres sur le sujet "Network fault model"
Wang, Jing, Jinglin Zhou et Xiaolu Chen. « Bayesian Causal Network for Discrete Variables ». Dans Intelligent Control and Learning Systems, 233–49. Singapore : Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8044-1_13.
Texte intégralSingh, Yogesh, Arvinder Kaur et Ruchika Malhotra. « Predicting Software Fault Proneness Model Using Neural Network ». Dans Lecture Notes in Business Information Processing, 215–17. Berlin, Heidelberg : Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-68255-4_26.
Texte intégralGeethu, N., et M. Rajesh. « 3G Cellular Network Fault Prediction Using LSTM-Conv1D Model ». Dans Lecture Notes in Networks and Systems, 323–36. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9967-2_31.
Texte intégralHe, Shuping, et Xiaoli Luan. « Neural Network-Based Robust Fault Detection for Nonlinear Multi-model Jumping System ». Dans Multi-model Jumping Systems : Robust Filtering and Fault Detection, 159–70. Singapore : Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6474-5_9.
Texte intégralDing, Shuo, Zhongyu Cheng, Qinghui Wu, Fang Zhang et Youlin Yang. « Transformer Fault Diagnosis Model Based on Discrete Hopfield Neural Network ». Dans Advances in Intelligent Systems and Computing, 1234–39. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98776-7_152.
Texte intégralHong, Won-Kyu, Dong-Il Kim, Seong-Sook Yoon, Seong-Ik Hong et Mun-Jo Jung. « Hierarchical Rerouting Model for Fault Tolerance in Multi-Network Environment ». Dans Managing QoS in Multimedia Networks and Services, 267–80. Boston, MA : Springer US, 2000. http://dx.doi.org/10.1007/978-0-387-35532-0_19.
Texte intégralHaibin, Yuan. « Network Topology Model and Fault Analysis for Electrical Control Systems ». Dans Electrical, Information Engineering and Mechatronics 2011, 1473–79. London : Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2467-2_175.
Texte intégralGong, Yi-shan, et Yang Li. « Motor Fault Diagnosis Based on Decision Tree-Bayesian Network Model ». Dans Advances in Intelligent and Soft Computing, 165–70. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28655-1_26.
Texte intégralUsman, Muhammad, Divya Gopinath, Youcheng Sun, Yannic Noller et Corina S. Păsăreanu. « NNrepair : Constraint-Based Repair of Neural Network Classifiers ». Dans Computer Aided Verification, 3–25. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_1.
Texte intégralChen, Tingting, Guanhong Zhang et Tong Wu. « Fault Detection of Rolling Bearings by Using a Combination Network Model ». Dans Machine Learning for Cyber Security, 390–99. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-20099-1_33.
Texte intégralActes de conférences sur le sujet "Network fault model"
Shao, Jiye, Rixin Wang, Jingbo Gao et Minqiang Xu. « Probabilistic Model-Based Fault Diagnosis of the Rotor System ». Dans ASME 2007 Power Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/power2007-22072.
Texte intégralShi, Zhanqun, Yibo Fan, Fengshou Gu, Abdul-Hannan Ali et Andrew Ball. « Neural Network Modelling Applied for Model-Based Fault Detection ». Dans ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58197.
Texte intégralGan, Chengyu, et Kourosh Danai. « Fault Diagnosis With a Model-Based Recurrent Neural Network ». Dans ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-2327.
Texte intégralKong, Changduk, Seonghee Kho, Jayoung Ki et Changho Lee. « A Study on Multi Fault Diagnostics of Smart Unmanned Aerial Vehicle Propulsion System Using Data Sorting and Neural Networks ». Dans ASME Turbo Expo 2008 : Power for Land, Sea, and Air. ASMEDC, 2008. http://dx.doi.org/10.1115/gt2008-50769.
Texte intégralRueda Villanoba, Sergio Alberto, et Carlos Borrás Pinilla. « Neural Network Based Fault Tolerant Control for a Semi-Active Suspension ». Dans ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-11516.
Texte intégralPatan, Krzysztof, et Jozef Korbicz. « Stable neural network based model predictive control ». Dans 2013 Conference on Control and Fault-Tolerant Systems (SysTol). IEEE, 2013. http://dx.doi.org/10.1109/systol.2013.6693895.
Texte intégralYao, Chen, Xi Yueyun, Chen Jinwei et Zhang Huisheng. « A Novel Gas Path Fault Diagnostic Model for Gas Turbine Based on Explainable Convolutional Neural Network With LIME Method ». Dans ASME Turbo Expo 2021 : Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/gt2021-59289.
Texte intégralFummi, Franco, Davide Quaglia et Francesco Stefanni. « Network Fault Model for Dependability Assessment of Networked Embedded Systems ». Dans 2008 23rd IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems (DFTVS). IEEE, 2008. http://dx.doi.org/10.1109/dft.2008.21.
Texte intégralCourdier, A., et Y. G. Li. « Power Setting Sensor Fault Detection and Accommodation for Gas Turbine Engines Using Artificial Neural Networks ». Dans ASME Turbo Expo 2016 : Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/gt2016-56304.
Texte intégralBodrumlu, Tolga, Mehmet Murat Gözüm et Batıkan Kavak. « Enhanced Fault Detection of Vehicle Lateral Dynamics Using a Dynamically Adjustable Bayesian Network Structure and Extented Kalman Filter ». Dans ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-94176.
Texte intégralRapports d'organisations sur le sujet "Network fault model"
Seginer, Ido, Louis D. Albright et Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, février 2001. http://dx.doi.org/10.32747/2001.7575271.bard.
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