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Literatura académica sobre el tema "Network fault model"

Autor: Grafiati

Publicado: 11 de marzo de 2023

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Artículos de revistas sobre el tema "Network fault model"

1

Bae, Jangsik, Meonghun Lee, and Changsun Shin. "A Data-Based Fault-Detection Model for Wireless Sensor Networks." Sustainability 11, no. 21 (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” optim

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2

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 (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 dev

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3

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 (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

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4

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 processin

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5

Wang, Zhenxing, Haijun Zhang, Huayang Wang, et al. "Analysis of modeling and fault line selection method for Single-phase Intermittent fault of distribution network." Journal of Physics: Conference Series 2355, no. 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 P

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6

Shakya, Subarna. "Pollination Inspired Clustering Model for Wireless Sensor Network Optimization." September 2021 3, no. 3 (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 i

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7

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 (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 Bayes

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8

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 (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,

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9

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

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10

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

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