Academic literature on the topic 'Network fault model'
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Journal articles on the topic "Network fault model"
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Bae, Jangsik, Meonghun Lee, and Changsun Shin. "A Data-Based Fault-Detection Model for Wireless Sensor Networks." Sustainability 11, no. 21 (November 5, 2019): 6171. http://dx.doi.org/10.3390/su11216171.
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With the expansion of smart agriculture, wireless sensor networks are being increasingly applied. These networks collect environmental information, such as temperature, humidity, and CO2 rates. However, if a faulty sensor node operates continuously in the network, unnecessary data transmission adversely impacts the network. Accordingly, a data-based fault-detection algorithm was implemented in this study to analyze data of sensor nodes and determine faults, to prevent the corresponding nodes from transmitting data; thus, minimizing damage to the network. A cloud-based “farm as a service” optimized for smart farms was implemented as an example, and resource management of sensors and actuators was provided using the oneM2M common platform. The effectiveness of the proposed fault-detection model was verified on an integrated management platform based on the Internet of Things by collecting and analyzing data. The results confirm that when a faulty sensor node is not separated from the network, unnecessary data transmission of other sensor nodes occurs due to continuous abnormal data transmission; thus, increasing energy consumption and reducing the network lifetime.
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Han, Bing, Xiaohui Yang, Yafeng Ren, and Wanggui Lan. "Comparisons of different deep learning-based methods on fault diagnosis for geared system." International Journal of Distributed Sensor Networks 15, no. 11 (November 2019): 155014771988816. http://dx.doi.org/10.1177/1550147719888169.
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The running state of a geared transmission system affects the stability and reliability of the whole mechanical system. It will greatly reduce the maintenance cost of a mechanical system to identify the faulty state of the geared transmission system. Based on the measured gear fault vibration signals and the deep learning theory, four fault diagnosis neural network models including fast Fourier transform–deep belief network model, wavelet transform–convolutional neural network model, Hilbert-Huang transform–convolutional neural network model, and comprehensive deep neural network model are developed and trained respectively. The results show that the gear fault diagnosis method based on deep learning theory can effectively identify various gear faults under real test conditions. The comprehensive deep neural network model is the most effective one in gear fault recognition.
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Shadi, Mohammad Reza, Hamid Mirshekali, Rahman Dashti, Mohammad-Taghi Ameli, and Hamid Reza Shaker. "A Parameter-Free Approach for Fault Section Detection on Distribution Networks Employing Gated Recurrent Unit." Energies 14, no. 19 (October 5, 2021): 6361. http://dx.doi.org/10.3390/en14196361.
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Faults in distribution networks can result in severe transients, equipment failure, and power outages. The quick and accurate detection of the faulty section enables the operator to avoid prolonged power outages and economic losses by quickly retrieving the network. However, the occurrence of diverse fault types with various resistances and locations and the highly non-linear nature of distribution networks make fault section detection challenging for numerous conventional techniques. This study presents a cutting-edge deep learning-based algorithm to distinguish fault sections in distribution networks to address these issues. The proposed gated recurrent unit model utilizes only two samples of the angle between the voltage and current on either side of the feeders, which record by smart feeder meters, to detect faulty sections in real time. When a network fault occurs, the protection relays trigger the trip command for the breakers. Immediately, the angle data are obtained from all smart feeder meters of the network, which comprises a pre-fault sample and a post-fault sample. The data are then employed as an input to the pre-trained gated recurrent unit model to determine the faulted line. The performance of this novel algorithm was validated through simulations of various fault types in the IEEE-33 bus system. The model recognizes the faulty section with competitive performance in terms of accuracy.
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Li, Zhi Chun. "A Simple SOM Neural Network Based Fault Detection Model for Fault Diagnosis of Rolling Bearings." Applied Mechanics and Materials 397-400 (September 2013): 1321–25. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.1321.
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Rolling bearings are common parts in the transmission systems and have been widely used in various kinds of applications. The normal operation of the rolling bearings hence plays an important role on the efficiency of the system performance. However, due to hostile working environment the rolling bearings are prone to failures. The transmission systems may break down when there occurs faults in the rolling bearings. As a result, it is essential to detect the faults of rolling bearings. However, when use artificial intelligence method to diagnose the rolling bearings faults the signal processing is extensively complex while very few works have been done on the simplification of the artificial neural network (ANN) models for the rolling bearings fault detection. To deal with this problem, a simple self-organized map (SOM) neural network method together with a principal component analysis (PCA) based feature reduction procedure is proposed to diagnosis rolling bearings faults in this work. The vibration data of the normal and faulty rolling bearings was acquired from an experimental test bed. The PCA was firstly used to extract distinct fault features. Then the SOM was employed to train and learn the fault features to identify the fault patterns. The fault detection results show that the proposed method is feasible and effective for the fault diagnosis of rolling bearings. The fault detection rate is beyond 89.0%.
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Wang, Zhenxing, Haijun Zhang, Huayang Wang, Zhijun Bi, Xiujing He, Qi Wang, and 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 (October 1, 2022): 012047. http://dx.doi.org/10.1088/1742-6596/2355/1/012047.
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Abstract Intermittent arcing often occurs when a single-phase-to-ground fault occurs in the distribution network. However, the intermittent fault modeling suitable for distribution network fault analysis is not perfect, the ability to handle intermittent arcs is insufficient, and fault line selection is prone to misjudgment. In this paper, based on analyzing the operating voltage and current characteristics of intermittent faults in the resonant grounding system of the distribution network, a simulation model of intermittent grounding faults of the 10kV distribution network is established in PSCAD/EMTDC, and a new method based on transient characteristics is proposed. The line selection method for intermittent faults in the distribution network based on fault transient characteristics is proposed. The simulation results show that the established model is suitable for fault analysis of distribution networks, and the proposed method of fault line selection is fast and correct.
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Shakya, Subarna. "Pollination Inspired Clustering Model for Wireless Sensor Network Optimization." September 2021 3, no. 3 (November 29, 2021): 196–207. http://dx.doi.org/10.36548/jsws.2021.3.006.
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Remote and dangerous fields that are expensive, complex, and unreachable to reach human insights are examined with ease using the Wireless Sensor Network (WSN) applications. Due to the use of non-renewable sources of energy, challenges with respect to the network lifetime, fault tolerance and energy consumption are faced by the self-managed networks. An efficient fault tolerance technique has been provided in this paper as an effective management strategy. Using the network and communication nodes, revitalization and fault recognition techniques are used for handling diverse levels of faults in this framework. At the network nodes, the fault tolerance capability is increased by the proposed protocol model and management strategy. This enhances the corresponding data transmission in the network. When compared to the conventional techniques, the proposed model increases the network lifetime by five times. It is observed from the validation results that, with a 10% increase in the network lifetime, there is a 2% decrease in the fault tolerance proficiency of the network. The network lifetime and data transmission rate are improved while the network energy consumption is reduced significantly. The MATLAB environment is used for simulation purpose. In terms of energy consumption, network lifetime and fault tolerance, the proposed model offers optimal results.
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Nai-Quan Su, Nai-Quan Su, Qing-Hua Zhang Nai-Quan Su, Shao-Lin Hu Qing-Hua Zhang, Xiao-Xiao Chang Shao-Lin Hu, and 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 (June 2022): 159–69. http://dx.doi.org/10.53106/199115992022063303013.
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<p>Increasing attention has been paid to the economic losses and personnel injuries caused by petrochemical gearbox faults. As a result, petrochemical enterprises started to pay huge attention on fault diagnosis technology to solve the fault diagnosis problem. Petrochemical gearboxes are characterized by many fault types, feature variables, and many-to-many relationships between the various fault parameters, which pose huge challenges in the fault diagnosis of petrochemical units. This paper proposes a petrochemical gearbox fault location and diagnosis method based on a distributed Bayesian model and neural network. The proposed approach is based on sample feature information and Bayesian network prior probability to construct a basic framework for petrochemical gearbox fault location. Neural network technology is used to to diagnose fault types. It is helpful to build a long-term fault diagnosis and monitoring system for rotating machinery of petrochemical units.</p> <p> </p>
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Patan, Krzysztof, and 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 (March 1, 2012): 225–37. http://dx.doi.org/10.2478/v10006-012-0017-6.
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Nonlinear model predictive control of a boiler unit: A fault tolerant control studyThis paper deals with a nonlinear model predictive control designed for a boiler unit. The predictive controller is realized by means of a recurrent neural network which acts as a one-step ahead predictor. Then, based on the neural predictor, the control law is derived solving an optimization problem. Fault tolerant properties of the proposed control system are also investigated. A set of eight faulty scenarios is prepared to verify the quality of the fault tolerant control. Based of different faulty situations, a fault compensation problem is also investigated. As the automatic control system can hide faults from being observed, the control system is equipped with a fault detection block. The fault detection module designed using the one-step ahead predictor and constant thresholds informs the user about any abnormal behaviour of the system even in the cases when faults are quickly and reliably compensated by the predictive controller.
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Basnet, Barun, Hyunjun Chun, and Junho Bang. "An Intelligent Fault Detection Model for Fault Detection in Photovoltaic Systems." Journal of Sensors 2020 (June 9, 2020): 1–11. http://dx.doi.org/10.1155/2020/6960328.
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Effective fault diagnosis in a PV system requires understanding the behavior of the current/voltage (I/V) parameters in different environmental conditions. Especially during the winter season, I/V characters of certain faulty states in a PV system closely resemble that of a normal state. Therefore, a normal fault detection model can falsely predict a well-operating PV system as a faulty state and vice versa. In this paper, an intelligent fault diagnosis model is proposed for the fault detection and classification in PV systems. For the experimental verification, various fault state and normal state datasets are collected during the winter season under wide environmental conditions. The collected datasets are normalized and preprocessed using several data-mining techniques and then fed into a probabilistic neural network (PNN). The PNN model will be trained with the historical data to predict and classify faults when new data is fetched in it. The trained model showed better performance in prediction accuracy when compared with other classification methods in machine learning.
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Zhang, Wubing. "Data Mining Technology for Equipment Machinery and Information Network Data Resources." Security and Communication Networks 2022 (August 3, 2022): 1–8. http://dx.doi.org/10.1155/2022/5928611.
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In order to solve the problem of aviation equipment system maintenance, it is very difficult to judge the faulty finished product according to the fault phenomenon, the author proposes a data mining-based prediction model for aviation equipment failure finished products. The model takes historical fault record data as input, clusters a large number of fault descriptions through text clustering to obtain fault phenomenon clusters, and establishes a many-to-many relationship between “fault phenomenon” and “fault finished product.” A probability distribution algorithm for faulty finished products is proposed, and by matching new fault phenomena and fault phenomenon clusters, the probability distribution of faulty finished products is calculated. The experimental results show that after calling the model to complete the clustering of the fault information database, 18966 fault phenomenon clusters are obtained, and each fault phenomenon cluster contains 2.9 fault records on average, the many-to-many relationship between the fault phenomenon and the faulty finished product of the fault information database is successfully constructed. The model can effectively predict the probability distribution of products that may fail according to the fault description, and the prediction accuracy can be improved with the increase of the amount of data to meet the actual security needs.
Dissertations / Theses on the topic "Network fault model"
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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.
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Thesis (M.S. in Information Technology Management and M.S. in Computer Science)--Naval Postgraduate School, September 2003.Thesis advisor(s): Geoffrey Xie, Alex Bordetsky. Includes bibliographical references (p. 47-48). Also available online.
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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.
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Memoria para optar al título de Ingeniero Civil MecánicoCon 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.
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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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Aull, 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.
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Almulla, 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.
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The most common fault type in electric power systems is the line to groundfault. In this type of faults, an electrical arc is usually developed. The thesispresents a mathematical model that describes the behavior of the arc during afault. The arc model has been verified based on real and simulated tests thatwere conducted on a system that has resonant earthing coil.In addition, two studies have been conducted on the same verified system.The first studied was implemented to see the effect of detuning the resonantearthing coil at different levels. It was noted that detuning the coil affected ACand the DC components in the arc faults. Also, the detuning affected the arcextinction.The second study has been looking at the effects of implementing a parallelresistor to the resonant earthing coil. The tests have been conducted usingdifferent set values of the resistor. In some of the studied cases and during thetesting period, the resistor has affected the self-extinguish behavior of the arc.Den 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.
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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.
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Real-time sensing brings the proliferation of big data that contains rich information of complex systems. It is well known that real-world systems show high levels of nonlinear and nonstationary behaviors in the presence of extraneous noise. This brings significant challenges for human experts to visually inspect the integrity and performance of complex systems from the collected data. My research goal is to develop innovative methodologies for modeling and optimizing complex systems, and create enabling technologies for real-world applications. Specifically, my research focuses on Mining Dynamic Recurrences in Nonlinear and Nonstationary Systems for Feature Extraction, Process Monitoring and Fault Diagnosis. This research will enable and assist in (i) sensor-driven modeling, monitoring and optimization of complex systems; (ii) integrating product design with system design of nonlinear dynamic processes; and (iii) creating better prediction/diagnostic tools for real-world complex processes.
My research accomplishments include the following.
(1) Feature Extraction and Analysis:
I proposed a novel multiscale recurrence analysis to not only delineate recurrence dynamics in complex systems, but also resolve the computational issues for the large-scale datasets. It was utilized to identify heart failure subjects from the 24-hour heart rate variability (HRV) time series and control the quality of mobile-phone-based electrocardiogram (ECG) signals.
(2) Modeling and Prediction:
I proposed the design of stochastic sensor network to allow a subset of sensors at varying locations within the network to transmit dynamic information intermittently, and a new approach of sparse particle filtering to model spatiotemporal dynamics of big data in the stochastic sensor network. It may be noted that the proposed algorithm is very general and can be potentially applicable for stochastic sensor networks in a variety of disciplines, e.g., environmental sensor network and battlefield surveillance network.
(3) Monitoring and Control:
Process monitoring of dynamic transitions in complex systems is more concerned with aperiodic recurrences and heterogeneous types of recurrence variations. However, traditional recurrence analysis treats all recurrence states homogeneously, thereby failing to delineate heterogeneous recurrence patterns. I developed a new approach of heterogeneous recurrence analysis for complex systems informatics, process monitoring and anomaly detection.
(4) Simulation and Optimization:
Another research focuses on fractal-based simulation to study spatiotemporal dynamics on fractal surfaces of high-dimensional complex systems, and further optimize spatiotemporal patterns. This proposed algorithm is applied to study the reaction-diffusion modeling on fractal surfaces and real-world 3D heart surfaces.
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Chauvin, 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.
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La restauration structurale a pour objectifs de déterminer la géométrie passée des roches et de valider les interprétations structurales. Les méthodes classiques sont basées sur des hypothèses géométriques et/ou cinématiques, et imposent un style de déformation. Les méthodes géomécaniques, en intégrant le comportement élastique des roches et les lois fondamentales de conservation mécanique, visent à résoudre les problèmes des méthodes classiques. Toutefois, il y a des incertitudes sur le choix des paramètres élastiques, et les contraintes de maillage rendent difficile l’utilisation de cette méthode comme un outil de validation des interprétations structurales. Le choix d’une méthode de restauration en particulier est rendu difficile par le fait qu’il y ait plusieurs approches de restauration géomécanique, en plus des nombreuses méthodes géométriques et cinématiques. Cette thèse présente en premier lieu une revue des différentes méthodes géomécaniques 3D visant à déplisser et annuler l’action des failles dans un modèle géologique. L’objectif de cette revue est de présenter les forces ainsi que les limites, théoriques et pratiques, de chaque méthode. Dans un second temps, à travers la restauration d’un modèle analogique (sandbox), nous présentons nos travaux sur le choix de conditions aux limites appropriées pour obtenir un modèle restauré cohérent. Ce modèle structural expérimental a été déformé en laboratoire et présente plusieurs analogies avec des structures extensives postérieures à une base salifère. Grâce à l’observation de l’évolution temporelle de la géométrie du modèle analogique sur une coupe, nous montrons qu’une condition aux limites correspondant à un raccourcissement latéral est nécessaire. Ce raccourcissement peut être estimé par la méthode de la surface transférée. De plus, nous définissons de nouvelles conditions aux limites de contacts de failles pour restaurer correctement le réseau de failles complexe du modèle analogique. Ces nouvelles conditions lient les bords internes des surfaces de failles et connectent les composantes connexes des failles coupées et déplacées par des failles plus récentes. Troisièmement, le test de différents paramètres élastiques indique que le module de Young, défini homogène au sein d’un modèle géologique, n’a quasiment pas d’effet sur le champ de déplacement. Toutefois, le coefficient de Poisson a un impact significatif sur la dilatation volumique. Dans un dernier temps, nous comparons la restauration géomécanique avec une méthode géométrique qui repose sur un modèle chronostratigraphique (GeoChron) qui fait une bijection de chaque point du sous-sol avec son équivalent dans l’espace de dépôt (Wheeler). Nous montrons que les deux approches de restauration fournissent des modèles restaurés du modèle analogique qui sont similaires géométriquement. La méthode géométrique a de nombreux avantages pour obtenir rapidement et avec précision le modèle restauré, mais elle manque de flexibilité sur le choix des contraintes de la déformation. La force de la méthode géomécanique est de pouvoir définir des conditions aux limites personnalisées et des comportements mécaniques spécifiques pour gérer les contextes mécaniquement complexesStructural 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
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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.
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Les machines à commutation de flux cinq-phases présentent une tolérance aux pannes et une robustesse qui les rendent très intéressantes dans un point de vue de la fiabilité, comme montré dans le premier chapitre.Dans ces travaux de thèse nous avons explorés la possibilité de détecter les défauts qui affectent cette machine par la signature des vibrations générées dans la machine.En utilisant les outils physiques et mathématiques présentés dans le deuxième chapitre, nous avons construit deux modèles multiphysiques, un modèle aux les éléments finis développé dans le troisième chapitre et un modèle analytique, appelé aux réseaux de perméances, dans le quatrième chapitre.Le comportement vibratoire de la machine a été étudié à l'aide de ces deux modèles, en régimesain et en défaut afin de connaitre comment ce comportement est influencé par les grandeurs électriques et magnétiques de la machine.Par ailleurs nous avons étudié la possibilité de détecter et discriminer les différents types de défauts.Le modèle analytique se présente comme un bon estimateur du comportement en défaut de la machine, malgré ses écarts avec la simulation.Dans le cinquième chapitre, les deux modèles multiphysiques ont été validés par des essais expérimentaux et nous avons pu expliquer le comportement en défaut d’un point de vue mécanique plutôt que magnétique.Enfin, dans le sixième chapitre, nous avons utilisé les deux modèles pour étudier le comportement en défaut de la machine, à des vitesses au-dessus de la limite expérimentale (3100 tr/min)Five-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)
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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.
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This master´s thesis deals with the indication and localization of earth faults in overhead lines. Earth fault is the most frequently occurring type of fault in medium voltage overhead lines – it covers approx. 95% of all faults and is very difficult to indicate and localize them correctly and in time with currently available methods on the market. Therefore is very important to study earth fault and its indication, localization. The thesis consists of a theoretical and a practical part. The theoretical part deals with faults in overhead networks with different type of neutral grounding, mainly with one phase to the ground fault in the compensated, ungrounded, solidly grounded and via resistance grounded networks. Most of the theoretical part is dedicated to one phase to the ground fault in the compensated and ungrounded networks, where this type fault is called the earth fault. In the compensated and ungrounded networks is described in details behavior – voltage and current relations during both steady state and transient state earth fault. The theoretical part is further dedicated to detection methods of earth faults and their preconditions for use. There is described also in details the complete procedure of earth fault detection, which includes indication, unhealthy feeder determination and exact position or line section localization. End of the theoretical part is then focused on determination of accuracy requirements for measurement of basic quantities and computation of other parameters. The practical part deals with a work at medium distribution network model, which includes familiarization with the model, detailed verification of its functionality and behavior during the earth fault, obtaining faults records and algorithmization of methods: method of qu – diagram and method of first half - period, which are able to detect unhealthy feeder. This part of the thesis was put together based on a demand of company Mega, corp., which wanted to verify function of both above mentioned and by them not yet tested methods.
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Chua, 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.
Full textBooks on the topic "Network fault model"
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Kumar, G. Prem. ATM network fault management using realistic abductive reasoning. Bangalore: Dept. of Electrical Communication Engineering, Indian Institute of Science, 1997.
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R, Callahan John, Whetten Brian, and United States. National Aeronautics and Space Administration., eds. Specification and design of a fault recovery model for the reliable multicast protocol. [Washington, DC]: National Aeronautics and Space Administration, 1996.
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Ding, Steven X. Model-Based Fault Diagnosis Techniques: Design Schemes, Algorithms and Tools. 2nd ed. London: Springer London, 2013.
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Butterfield, A. Memory models: A formal analysis using VDM. Dublin: Trinity College, Department of Computer Science, 1992.
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L, Montgomery Todd, Whetten Brian, and United States. National Aeronautics and Space Administration., eds. Fault recovery in the Reliable Multicast Protocol. [Washington, D.C.]: National Aeronautics and Space Administration, 1995.
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Ding, Steven X. Model-Based Fault Diagnosis Techniques. Springer, 2008.
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Bayesian Networks in Fault Diagnosis: Practice and Application. WSPC, 2018.
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Model-Based Fault Diagnosis Techniques: Design Schemes, Algorithms and Tools. Springer London, Limited, 2015.
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Ding, Steven X. Model-Based Fault Diagnosis Techniques: Design Schemes, Algorithms and Tools. Springer, 2013.
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Walter, Eric, and Richard Walter. Data Acquisition from Light-Duty Vehicles Using OBD and CAN. SAE International, 2018. http://dx.doi.org/10.4271/r-458.
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Modern vehicles have multiple electronic control units (ECU) to control various subsystems such as the engine, brakes, steering, air conditioning, and infotainment. These ECUs are networked together to share information directly with each other. This in-vehicle network provides a data opportunity for improved maintenance, fleet management, warranty and legal issues, reliability, and accident reconstruction. Data Acquisition from Light-Duty Vehicles Using OBD and CAN is a guide for the reader on how to acquire and correctly interpret data from the in-vehicle network of light-duty (LD) vehicles. The reader will learn how to determine what data is available on the vehicle's network, acquire messages and convert them to scaled engineering parameters, apply more than 25 applicable standards, and understand 15 important test modes. Topics featured in this book include: • Calculated fuel economy • Duty cycle analysis • Capturing intermittent faults Written by two specialists in this field, Richard P. Walter and Eric P. Walter of HEM Data, the book provides a unique roadmap for the data acquisition user. The authors give a clear and concise description of the CAN protocol plus a review of all 19 parts of the SAE International J1939 standard family. Data Acquisition from Light-Duty Vehicles Using OBD and CAN is a must-have reference for product engineers, service technicians fleet managers and all interested in acquiring data effectively from the light-duty vehicles.
Book chapters on the topic "Network fault model"
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Wang, Jing, Jinglin Zhou, and Xiaolu Chen. "Bayesian Causal Network for Discrete Variables." In Intelligent Control and Learning Systems, 233–49. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8044-1_13.
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AbstractEnsuring the safety of industrial systems requires not only detecting the faults, but also locating them so that they can be eliminated. The previous chapters have discussed the fault detection and identification methods. Fault traceability is also an important issue in industrial system. This chapter and Chap. 10.1007/978-981-16-8044-1_14 aim at the fault inference and root tracking based on the probabilistic graphical model. This model explores the internal linkages of system variables quantitatively and qualitatively, so it avoids the bottleneck of multivariate statistical model without clear mechanism. The exacted features or principle components of multivariate statistical model are linear or nonlinear combinations of system variables and have not any physical meaning. So the multivariate statistical model is good at fault detection and identification, but not at fault root tracking.
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Singh, Yogesh, Arvinder Kaur, and Ruchika Malhotra. "Predicting Software Fault Proneness Model Using Neural Network." In 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.
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Geethu, N., and M. Rajesh. "3G Cellular Network Fault Prediction Using LSTM-Conv1D Model." In 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.
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He, Shuping, and Xiaoli Luan. "Neural Network-Based Robust Fault Detection for Nonlinear Multi-model Jumping System." In 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.
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Ding, Shuo, Zhongyu Cheng, Qinghui Wu, Fang Zhang, and Youlin Yang. "Transformer Fault Diagnosis Model Based on Discrete Hopfield Neural Network." In 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.
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Hong, Won-Kyu, Dong-Il Kim, Seong-Sook Yoon, Seong-Ik Hong, and Mun-Jo Jung. "Hierarchical Rerouting Model for Fault Tolerance in Multi-Network Environment." In 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.
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Haibin, Yuan. "Network Topology Model and Fault Analysis for Electrical Control Systems." In Electrical, Information Engineering and Mechatronics 2011, 1473–79. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2467-2_175.
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Gong, Yi-shan, and Yang Li. "Motor Fault Diagnosis Based on Decision Tree-Bayesian Network Model." In 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.
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Usman, Muhammad, Divya Gopinath, Youcheng Sun, Yannic Noller, and Corina S. Păsăreanu. "NNrepair: Constraint-Based Repair of Neural Network Classifiers." In Computer Aided Verification, 3–25. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_1.
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AbstractWe present NNrepair, a constraint-based technique for repairing neural network classifiers. The technique aims to fix the logic of the network at an intermediate layer or at the last layer. NNrepair first uses fault localization to find potentially faulty network parameters (such as the weights) and then performs repair using constraint solving to apply small modifications to the parameters to remedy the defects. We present novel strategies to enable precise yet efficient repair such as inferring correctness specifications to act as oracles for intermediate layer repair, and generation of experts for each class. We demonstrate the technique in the context of three different scenarios: (1) Improving the overall accuracy of a model, (2) Fixing security vulnerabilities caused by poisoning of training data and (3) Improving the robustness of the network against adversarial attacks. Our evaluation on MNIST and CIFAR-10 models shows that NNrepair can improve the accuracy by 45.56% points on poisoned data and 10.40% points on adversarial data. NNrepair also provides small improvement in the overall accuracy of models, without requiring new data or re-training.
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Chen, Tingting, Guanhong Zhang, and Tong Wu. "Fault Detection of Rolling Bearings by Using a Combination Network Model." In Machine Learning for Cyber Security, 390–99. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-20099-1_33.
Full textConference papers on the topic "Network fault model"
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Shao, Jiye, Rixin Wang, Jingbo Gao, and Minqiang Xu. "Probabilistic Model-Based Fault Diagnosis of the Rotor System." In ASME 2007 Power Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/power2007-22072.
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The rotor is one of the most core components of the rotating machinery and its working states directly influence the working states of the whole rotating machinery. There exists much uncertainty in the field of fault diagnosis in the rotor system. This paper analyses the familiar faults of the rotor system and the corresponding faulty symptoms, then establishes the rotor’s Bayesian network model based on above information. A fault diagnosis system based on the Bayesian network model is developed. Using this model, the conditional probability of the fault happening is computed when the observation of the rotor is presented. Thus, the fault reason can be determined by these probabilities. The diagnosis system developed is used to diagnose the actual three faults of the rotor of the rotating machinery and the results prove the efficiency of the method proposed.
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Shi, Zhanqun, Yibo Fan, Fengshou Gu, Abdul-Hannan Ali, and Andrew Ball. "Neural Network Modelling Applied for Model-Based Fault Detection." In ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58197.
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This paper aims to combine neural network modelling with model-based fault detection. An accurate and robust model is critical in model-based fault detection. However, the development of such a model is the most difficult task especially when a non-linear system is involved. The problem comes not only from the lack of concerned information about model parameters, but also from the inevitable linearization. In order to solve this problem, neural networks are introduced in this paper. Instead of using conventional neural network modelling, the neural network is only used to approximate the non-linear part of the system, leaving the linear part to be represented by a mathematical model. This new scheme of integration between neural network and mathematical model (NNMM) allows the compensation of the error from conventional modelling methods. Simultaneously, it keeps the residual signatures physically interpretable.
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Gan, Chengyu, and Kourosh Danai. "Fault Diagnosis With a Model-Based Recurrent Neural Network." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-2327.
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Abstract The utility of a model-based recurrent neural network (MBRNN) is demonstrated in fault diagnosis. The MBRNN can be formatted according to a state-space model. Therefore, it can use model-based fault detection and isolation (FDI) solutions as a starting point, and improve them via training by adapting them to plant nonlinearities. In this paper, the application of MBRNN to the IFAC Benchmark Problem is explored and its performance is compared with ‘black box’ neural network solutions. For this problem, the MBRNN is formulated according to the Eigen-Structure Assignment (ESA) residual generator developed by Jorgensen et al. [1]. The results indicate that the MBRNN provides better results than ‘black box’ neural networks, and that with training it can perform better than the ESA residual generator.
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Kong, Changduk, Seonghee Kho, Jayoung Ki, and Changho Lee. "A Study on Multi Fault Diagnostics of Smart Unmanned Aerial Vehicle Propulsion System Using Data Sorting and Neural Networks." In ASME Turbo Expo 2008: Power for Land, Sea, and Air. ASMEDC, 2008. http://dx.doi.org/10.1115/gt2008-50769.
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The types and severities of most engine faults are so complex that it is not easy for a conventional model based fault diagnosis approach like the GPA (Gas Path Analysis) method be used to monitor all engine fault conditions. This study therefore discusses on the newly proposed diagnostic algorithm for isolating and effectively identifying the faulted components of the smart UAV propulsion system, that has been developed by KARI (Korea Aerospace Research Institute) based on the fuzzy logic and the neural network algorithms. The diagnosis procedure of the proposed diagnostic system has the following steps. First obtaining database of fault patterns through performance simulation, followed by training the database using the FFBP networks. The third step involve analyzing the trend of the measured parameters due to fault patterns, linked to this is the fourth step that involve isolating the faulty components using fuzzy logic, and finally the magnitudes of the detected faults are obtained by the trained neural networks. The analysis showed that the detected faults had almost same degradation values to those of the implanted fault pattern, confirming that the proposed diagnostic system can be used to effectively detect the engine faults.
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Rueda Villanoba, Sergio Alberto, and Carlos Borrás Pinilla. "Neural Network Based Fault Tolerant Control for a Semi-Active Suspension." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-11516.
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Abstract In this study a Neural Network based fault tolerant control is proposed to accommodate oil leakages in a magnetorheological suspension system based in a half car dynamic model. This model consists of vehicle body (spring mass) connected by the MR suspension system to two lateral wheels (unsprung mass). The semi-active suspension system is a four states nonlinear model; it can be written as a state space representation. The main objectives of a suspension are: Isolate the chassis from road disturbances (passenger comfort) and maintain contact between tire and road to provide better maneuverability, safety and performance. On the other hand, component faults/failures are inevitable in all practical systems, the shock absorbers of semi-active suspensions are prone to fail due to fluid leakage but quickly detect and diagnose this fault in the system, avoid major damage to the system and ensure the safety of the driver. To successfully achieve desirable control performance, it is necessary to have a damping force model which can accurately represent the highly nonlinear and hysteretic dynamic of the MR damper. To simulate parameters of the damper, a quasi-static model was applied, quasi-static approaches are based on non-newtonian yield stress fluids flow by using the Bingham MR Damper Model, relating the relative displacement of the piston, the frictional force, a damping constant, the stiffness of the elastic element of the damper and an offset force. The Fault detection and isolation module is based on residual generation algorithms. The residua r is computed as the difference between the displacement signal of functional and faulty model, when the residual is close to zero, the process is free of faults, while any change in r represents a faulty scheme then a wavelet transform, (Morlet wave function) is used to determine the natural frequencies and amplitudes of displacement and acceleration signal during the failure, this module provides parameters to the neural network controller in order to accommodate the failure using compensation forces from the remaining healthy damper. The neural network uses the error between the plant output and the neural network plant for computing the required electric current to correct the malfunction using the inverse dynamics function of the MR damper model. Consequently, a bump condition, and a random profile road (ISO 8608) described by the power spectral density (PSD) of its vertical displacement, is used as disturbance of control system. The performance of the proposed FTC structure is demonstrated trough simulation. Results shows that the control system could reduce the effect of the partial fault of the MR Damper on system performance.
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Patan, Krzysztof, and Jozef Korbicz. "Stable neural network based model predictive control." In 2013 Conference on Control and Fault-Tolerant Systems (SysTol). IEEE, 2013. http://dx.doi.org/10.1109/systol.2013.6693895.
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Yao, Chen, Xi Yueyun, Chen Jinwei, and Zhang Huisheng. "A Novel Gas Path Fault Diagnostic Model for Gas Turbine Based on Explainable Convolutional Neural Network With LIME Method." In ASME Turbo Expo 2021: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/gt2021-59289.
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Abstract Gas turbine is widely used in aviation and energy industries. Gas path fault diagnosis is an important task for gas turbine operation and maintenance. With the development of information technology, especially deep learning methods, data-driven approaches for gas path diagnosis are developing rapidly in recent years. However, the mechanism of most data-driven models are difficult to explain, resulting in lacking of the credibility of the data-driven methods. In this paper, a novel explainable data-driven model for gas path fault diagnosis based on Convolutional Neural Network (CNN) using Local Interpretable Model-agnostic Explanations (LIME) method is proposed. The input matrix of CNN model is established by considering the mechanism information of gas turbine fault modes and their effects. The relationship between the measurement parameters and fault modes are considered to arrange the relative position in the input matrix. The key parameters which contributes to fault recognition can be achieved by LIME method, and the mechanism information is used to verify the fault diagnostic proceeding and improve the measurement sensor matrix arrangement. A double shaft gas turbine model is used to generate healthy and fault data including 12 typical faults to test the model. The accuracy and interpretability between the CNN diagnosis model built with prior mechanism knowledge and built by parameter correlation matrix are compared, whose accuracy are 96.34% and 89.46% respectively. The result indicates that CNN diagnosis model built with prior mechanism knowledge shows better accuracy and interpretability. This method can express the relevance of the failure mode and its high-correlation measurement parameters in the model, which can greatly improve the interpretability and application value.
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Fummi, Franco, Davide Quaglia, and Francesco Stefanni. "Network Fault Model for Dependability Assessment of Networked Embedded Systems." In 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.
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Courdier, A., and Y. G. Li. "Power Setting Sensor Fault Detection and Accommodation for Gas Turbine Engines Using Artificial Neural Networks." In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/gt2016-56304.
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Sensors installed on gas turbine gas path are used to obtain gas path measurement parameters for control and condition monitoring purpose. These sensors are prone to degradation or failure due to hostile working environment around them. Most gas path sensor diagnostic research is based on an assumption that the power setting sensor, a sensor used by engine control system to control engine power output, has no fault so engine measurement data can always be obtained at desired operating conditions. However in practice, power setting sensor may also be faulty, which may result in misleading measurement data and diagnostic results. In this paper, an artificial neural network based gas path diagnostic approach for engine power setting sensor fault detection and quantification has been introduced. Nested artificial neural networks (ANN) are used to detect power setting sensor fault and ensure prediction accuracy. Measurement noise is also considered in the training and testing samples to ensure the robustness of the diagnostic system. The developed power setting sensor diagnostic approach has been applied to a model 2-shafts industrial gas turbine engine similar to a GE LM2500+G4 engine to test the effectiveness of the approach. The selected power setting parameter is the shaft power output measured by a power setting sensor. An engine performance model is produced using Cranfield University’s gas turbine performance and diagnostics software, Pythia. Training samples with the consideration of sensor faults were simulated with the engine model assuming one of the sensors, either the power setting sensor or other gas path sensors may be faulty. In the nested neural network for sensor fault diagnostics, the system separately performs sensor fault detection, sensor fault identification and sensor fault quantification. Results show that the developed nested neural network diagnostic system is able to identify the power setting sensor fault and correctly predict the magnitude of the fault. This would allow the engine control system correct its control schedule and accommodate the power setting sensor fault.
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Bodrumlu, Tolga, Mehmet Murat Gözüm, and Batıkan Kavak. "Enhanced Fault Detection of Vehicle Lateral Dynamics Using a Dynamically Adjustable Bayesian Network Structure and Extented Kalman Filter." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-94176.
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Abstract In order to improve reliability and safety in many technical systems; the importance of the fault diagnosis and detection is increasing day by day. This is particularly important in safety-related industries such as airplanes, trains, automobiles, power plants, and chemical plants. In this study, a model-based fault detection method that can detect faults affecting the lateral dynamic system of a vehicle is proposed. The developed fault detection algorithm includes both Bayesian Network and Extended Kalman Filter (EKF). EKF plays an effective role especially in detecting the faulty speed measurements. In the fault detection algorithm, six residual values are calculated. The threshold values of all the calculated residuals are determined using real test dataset. Depending on whether the residuals exceed the threshold value or not, the fault generation coefficients in the Bayesian Network are also dynamically updated to provide precise information regarding which sensor has a fault. The implementation of the fault detection algorithm is carried out using real test data and the numerical simulations are performed in MATLAB/Simulink environment. The results show that the proposed fault detection algorithm gives over 92% fault probability by using Bayesian and EKF structure together.
Reports on the topic "Network fault model"
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Seginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, February 2001. http://dx.doi.org/10.32747/2001.7575271.bard.
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Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, control, structure and crop). 2. Using these models, develop algorithms for an early detection of deviations from the normal. 3. Develop identifying procedures for the most important faults. 4. Develop accommodation procedures while awaiting a repair. The Technion team focused on the shoot environment and the Cornell University team focused on the root environment. Achievements Models: Accurate models were developed for both shoot and root environment in the greenhouse, utilizing neural networks, sometimes combined with robust physical models (hybrid models). Suitable adaptation methods were also successfully developed. The accuracy was sufficient to allow detection of frequently occurring sensor and equipment faults from common measurements. A large data base, covering a wide range of weather conditions, is required for best results. This data base can be created from in-situ routine measurements. Detection and isolation: A robust detection and isolation (formerly referred to as 'identification') method has been developed, which is capable of separating the effect of faults from model inaccuracies and disturbance effects. Sensor and equipment faults: Good detection capabilities have been demonstrated for sensor and equipment failures in both the shoot and root environment. Water stress detection: An excitation method of the shoot environment has been developed, which successfully detected water stress, as soon as the transpiration rate dropped from its normal level. Due to unavailability of suitable monitoring equipment for the root environment, crop faults could not be detected from measurements in the root zone. Dust: The effect of screen clogging by dust has been quantified. Implications Sensor and equipment fault detection and isolation is at a stage where it could be introduced into well equipped and maintained commercial greenhouses on a trial basis. Detection of crop problems requires further work. Dr. Peleg was primarily responsible for developing and implementing the innovative data analysis tools. The cooperation was particularly enhanced by Dr. Peleg's three summer sabbaticals at the ARS, Northem Plains Agricultural Research Laboratory, in Sidney, Montana. Switching from multi-band to hyperspectral remote sensing technology during the last 2 years of the project was advantageous by expanding the scope of detected plant growth attributes e.g. Yield, Leaf Nitrate, Biomass and Sugar Content of sugar beets. However, it disrupted the continuity of the project which was originally planned on a 2 year crop rotation cycle of sugar beets and multiple crops (com and wheat), as commonly planted in eastern Montana. Consequently, at the end of the second year we submitted a continuation BARD proposal which was turned down for funding. This severely hampered our ability to validate our findings as originally planned in a 4-year crop rotation cycle. Thankfully, BARD consented to our request for a one year extension of the project without additional funding. This enabled us to develop most of the methodology for implementing and running the hyperspectral remote sensing system and develop the new analytical tools for solving the non-repeatability problem and analyzing the huge hyperspectral image cube datasets. However, without validation of these tools over a ful14-year crop rotation cycle this project shall remain essentially unfinished. Should the findings of this report prompt the BARD management to encourage us to resubmit our continuation research proposal, we shall be happy to do so.