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Journal articles on the topic 'Network fault model'

Author: Grafiati

Published: 11 March 2023

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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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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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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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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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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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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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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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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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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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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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11

Hoang, Ngoc-Bach, and Hee-Jun Kang. "Incipient wheel fault identification in mobile robots using neural networks and nonlinear least squares." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 3 (2016): 446–58. http://dx.doi.org/10.1177/0954406215616650.

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In this paper, we present a novel method for fault identification in the case of an incipient wheel fault in mobile robots. First, a three-layer neural networks is established to estimate the deviation of the robot dynamics due to the process fault. The estimate of the faulty dynamic model is based on a combination of the nominal dynamic model and the neural network output. Then, by replacing the faulty dynamic model with its estimate value, the primary estimates of the wheel radius appear as the solutions of two quadratic equations. Next, a simple and efficient way to perform these primary es

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Jha, Sudan, Sultan Ahmad, Deepak Prashar, et al. "A Proposed Waiting Time Algorithm for a Prediction and Prevention System of Traffic Accidents Using Smart Sensors." Electronics 11, no. 11 (2022): 1765. http://dx.doi.org/10.3390/electronics11111765.

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One of the applications of neural networks is to predict the fault section results of traffic utilizing the combined model estimation of the fault section and self-learning models with smart sensors. The prediction of the fault section can autonomously develop the internal model of the network to fit the pre-entered “traffic accident” section data and predict the occurrence of traffic accident sections. In this paper, we propose the results of waiting time for traffic accidents in case of traffic accidents by using a neural network and fuzzy expert system, in comparison with existing algorithm

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Lakehal, Abdelaziz, and Fouad Tachi. "Bayesian Duval Triangle Method for Fault Prediction and Assessment of Oil Immersed Transformers." Measurement and Control 50, no. 4 (2017): 103–9. http://dx.doi.org/10.1177/0020294017707461.

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Dissolved gas analysis of transformer insulating oil is considered the best indicator of a transformer’s overall condition and is most widely used. In this study, a Bayesian network was developed to predict failures of electrical transformers. The Duval triangle method was used to develop the Bayesian model. The proposed prediction model represents a transformer fault prediction, possible faulty behaviors produced by this transformer (symptoms), along with results of possible dissolved gas analysis. The model essentially captures how possible faults of a transformer can manifest themselves by

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Wang, Qianyu, Dong Cao, Shuyuan Zhang, Yuzan Zhou, and Lina Yao. "The Cable Fault Diagnosis for XLPE Cable Based on 1DCNNs-BiLSTM Network." Journal of Control Science and Engineering 2023 (January 19, 2023): 1–10. http://dx.doi.org/10.1155/2023/1068078.

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Diagnosing the fault type accurately from a variety of faults is very essential to ensure a stable electricity supply when a short-circuit fault occurs. In this paper, a hybrid classification model combining the one-dimensional convolutional neural network (1D-CNN) and the bidirectional long short-term memory network (BiLSTM) is proposed for the classification of cable short-circuit faults to improve the accuracy of fault diagnosis. Sample sets of the current signal for single-phase grounding short circuit, two-phase grounding short circuit, two-phase to phase short circuit, and three-phase gr

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15

Cui, Hao Yang, Yong Peng Xu, Jun Jie Yang, Jun Dong Zeng, and Zhong Tang. "A Fault Diagnosis Method in VSC-HVDC Simulation System Based on BRBP Neural Networks." Advanced Materials Research 860-863 (December 2013): 2269–74. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.2269.

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As the feature of faulty signal in high voltage direct current transmission technology based on voltage source converter (VSC-HVDC) system is complicated to extract and its difficult to carry on the fault diagnosis. On the basis of the PSCAD simulation model of VSC-HVDC system, the DC current faulty signal is analyzed. Then, the wavelet analysis method was adopted to extract the eigenvector of faulty signal, and combined with method of Bayesian regularization back-propagation (BRBP) neural networks, the system fault was identified. The simulation results show that the method is more efficientl

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16

Su, Shan, and Bing Sheng Yan. "Fault Location Algorithm of the 10kV Rural Network Based on Power Frequency Communication." Advanced Materials Research 722 (July 2013): 287–91. http://dx.doi.org/10.4028/www.scientific.net/amr.722.287.

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A new fault locating model of multi-ports data of 10kV village distributed network has been constructed abased on the basic theory of liner circurt. by taking fault ports as boundaries, the faulted power system is divided into a symmetrical network without the faults and unsymmetrical networks employed to simulate the faults. In this modell, The locating of fault point is looked as an unknowed variable and it is included in the Zbus of unsymmetrical network. We can get it though solving the equation. The method does not need to modify the Zbus of original sequence network and easy implementati

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17

Guoyan, Huang, Wang Qian, Liu Xinqian, Hao Xiaobing, and Yan Huaizhi. "Mining the Key Nodes from Software Network Based on Fault Accumulation and Propagation." Security and Communication Networks 2019 (March 7, 2019): 1–11. http://dx.doi.org/10.1155/2019/7140480.

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The increasement of software complexity directly results in the augment of software fault and costs a lot in the process of software development and maintenance. The complex network model is used to study the accumulation and accumulation of faults in complex software as a whole. Then key nodes with high fault probability and powerful fault propagation capability can be found, and the faults can be discovered as soon as possible and the severity of the damage to the system can be reduced effectively. In this paper, the algorithm MFS_AN (mining fault severity of all nodes) is proposed to mine t

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18

Farsoni, Saverio, Silvio Simani, and Paolo Castaldi. "Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis." Applied Sciences 11, no. 11 (2021): 5035. http://dx.doi.org/10.3390/app11115035.

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The fault diagnosis of safety critical systems such as wind turbine installations includes extremely challenging aspects that motivate the research issues considered in this paper. Therefore, this work investigates two fault diagnosis solutions that exploit the direct estimation of the faults by means of data-driven approaches. In this way, the diagnostic residuals are represented by the reconstructed faults affecting the monitored process. The proposed methodologies are based on fuzzy systems and neural networks used to estimate the nonlinear dynamic relations between the input and output mea

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19

Robson, Stephen, Abderrahmane Haddad, and Huw Griffiths. "Traveling Wave Fault Location Using Layer Peeling." Energies 12, no. 1 (2018): 126. http://dx.doi.org/10.3390/en12010126.

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Many fault-location algorithms rely on a simulation model incorporating network parameters which closely represent the real network. Estimations of the line parameters are usually based on limited geometrical information which do not reflect the complexity of a real network. In practice, obtaining an accurate model of the network is difficult without comprehensive field measurements of each constituent part of the network in question. Layer-peeling algorithms offer a solution to this problem by providing a fast “mapping” of the network based only on the response of a probing impulse. Starting

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Dorsett, Jacob H., Elizabeth H. Madden, Scott T. Marshall, and Michele L. Cooke. "Mechanical Models Suggest Fault Linkage through the Imperial Valley, California, U.S.A." Bulletin of the Seismological Society of America 109, no. 4 (2019): 1217–34. http://dx.doi.org/10.1785/0120180303.

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Abstract The Imperial Valley hosts a network of active strike‐slip faults that comprise the southern San Andreas fault (SAF) and San Jacinto fault systems and together accommodate the majority of relative Pacific–North American plate motion in southern California. To understand how these faults partition slip, we model the long‐term mechanics of four alternative fault networks with different degrees of connectivity through the Imperial Valley using faults from the Southern California Earthquake Center Community Fault Model version 5.0 (v.5.0). We evaluate model results against average fault‐sl

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Lal, Jaya Dipti, and Dolly Thankachan. "HBMFTEFR: Design of a Hybrid Bioinspired Model for Fault-Tolerant Energy Harvesting Networks via Fuzzy Rule Checks." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 1s (2022): 166–81. http://dx.doi.org/10.17762/ijritcc.v10i1s.5821.

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Designing energy harvesting networks requires modelling of energy distribution under different real-time network conditions. These networks showcase better energy efficiency, but are affected by internal & external faults, which increase energy consumption of affected nodes. Due to this probability of node failure, and network failure increases, which reduces QoS (Quality of Service) for the network deployment. To overcome this issue, various fault tolerance & mitigation models are proposed by researchers, but these models require large training datasets & real-time samples for eff

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Zhang, Chunhua, Wen Fang, Baopeng Zhao, et al. "Study on Fault Diagnosis Method and Application of Automobile Power Supply Based on Fault Tree-Bayesian Network." Security and Communication Networks 2022 (May 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/4046966.

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Among various fault types of automotive power faults, power supply UB + faults have the most complex relationship between fault signs and fault points and are difficult to diagnose. So this paper proposes a Bayesian network fault diagnosis model of automobile power supply based on fault tree. Firstly, based on the in-depth analysis of the principle of automobile power supply fault, the UB + fault tree model is constructed. The fuzzy Bayesian network model of UB + fault is constructed through the mapping relationship between fault tree and Bayesian network. Then, the prior probability of UB + f

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Nikanjam, Amin, Houssem Ben Braiek, Mohammad Mehdi Morovati, and Foutse Khomh. "Automatic Fault Detection for Deep Learning Programs Using Graph Transformations." ACM Transactions on Software Engineering and Methodology 31, no. 1 (2022): 1–27. http://dx.doi.org/10.1145/3470006.

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Nowadays, we are witnessing an increasing demand in both corporates and academia for exploiting Deep Learning ( DL ) to solve complex real-world problems. A DL program encodes the network structure of a desirable DL model and the process by which the model learns from the training dataset. Like any software, a DL program can be faulty, which implies substantial challenges of software quality assurance, especially in safety-critical domains. It is therefore crucial to equip DL development teams with efficient fault detection techniques and tools. In this article, we propose NeuraLint , a model-

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Jain, Rishabh, and Umesh Sajjanar. "Pro-active Performance Monitoring in Optical Networks using Frequency Aware Seq2Seq Model." Indian Journal of Data Communication and Networking 3, no. 2 (2023): 1–10. http://dx.doi.org/10.54105/ijdcn.b5028.023223.

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Performance Monitoring (PM) and Fault Detection have always been a reactionary approach in Optical Networks for most service providers. Any kind of fault (power surge, ageing issues, equipment faults and failures, natural calamities, etc.) in an optical network is detected only after the fault has occurred and mitigation is performed afterward. The resultant service outages for end-users cause huge financial and reputation losses to the vendors. Therefore, there is a strong need for proactive detection of faults to limit disruption and provide uninterrupted services to clients. We achieve this

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Singh, Seema, and T. V. Rama Murthy. "Neural Network-Based Sensor Fault Accommodation in Flight Control System." Journal of Intelligent Systems 22, no. 3 (2013): 317–33. http://dx.doi.org/10.1515/jisys-2013-0032.

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AbstractThis article deals with detection and accommodation of sensor faults in longitudinal dynamics of an F8 aircraft model. Both the detection of the fault and reconfiguration of the failed sensor are done with the help of neural network-based models. Detection of a sensor fault is done with the help of knowledge-based neural network fault detection (KBNNFD). Apart from KBNNFD, another neural network model is developed in this article for the reconfiguration of the failed sensor. A model-based approach of the neural network (MBNN) is developed, which uses the radial basis function of the ne

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Li, Jing, Yuxing Yang, and Xiaohui Gao. "Hamiltonicity of the Torus Network Under the Conditional Fault Model." International Journal of Foundations of Computer Science 28, no. 03 (2017): 211–27. http://dx.doi.org/10.1142/s0129054117500149.

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Low-dimensional Tori are regularly used as interconnection networks in distributed-memory parallel computers. This paper investigates the fault-Hamiltonicity of two-dimensional Tori. A sufficient condition is derived for the graph Row-Torus(m, 2n + 1) with two faulty edges to have a Hamiltonian cycle, where m ≥ 3 and n ≥ 1. By applying the fault-Hamiltonicity of Row-Torus to a two-dimensional torus, we show that Torus(m, n), m, n ≥ 5, with at most four faulty edges is Hamiltonian if the following two conditions are satisfied: (1) the degree of every vertex is at least two, and (2) there do not

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Liu, Bohai, Qinmu Wu, Zhiyuan Li, and Xiangping Chen. "Research on Fault Diagnosis of IPMSM for Electric Vehicles Based on Multi-Level Feature Fusion SPP Network." Symmetry 13, no. 10 (2021): 1844. http://dx.doi.org/10.3390/sym13101844.

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At this stage, the fault diagnosis of the embedded permanent magnet synchronous motor (IPMSM) mostly relies on the analysis of related signals when the motor is running. It requires designers to deeply understand the motor drive system and fault characteristic signals, which leads to a high threshold for fault diagnosis. This study proposes an IPMSM fault diagnosis method based on a multi-level feature fusion spatial pyramid pooling (SPP) network, which can directly diagnose motor faults through motor operating current data. This method uses the finite element software Altair Flux to build sym

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Bian, Li, and Chen Yuan Bian. "Fault Diagnosis Method for Power Network Based on Combinational Cross Entropy Algorithm." Applied Mechanics and Materials 548-549 (April 2014): 851–54. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.851.

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A new method of fault diagnosis for power networks by using the combinatorial cross entropy (CCE) algorithm is proposed. The research contents in this paper mainly contain the two parts: transmission network fault diagnosis and distribution network fault location. For transmission network, the optimization model is built by considering the relationship among fault elements, action information of protective relays and circuit breakers. For distribution network, constructing fault location model according to the logic relationship between fault current and equipment condition. The optimal soluti

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Jawad, Raad Salih, and Hafedh Abid. "HVDC Fault Detection and Classification with Artificial Neural Network Based on ACO-DWT Method." Energies 16, no. 3 (2023): 1064. http://dx.doi.org/10.3390/en16031064.

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Unlike the more prevalent alternating current transmission systems, the high voltage direct current (HVDC) electric power transmission system transmits electric power using direct current. In order to investigate the precise remedy for fault detection of HVDC, this research proposes a method for the HVDC fault diagnostic methodologies with their limits and feature selection-based probabilistic generative model. The main contribution of this study is using the wavelet transform based on ant colony optimization and ANN to detect the different types of faults in HVDC transmission lines. In the pr

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Tariq, Rizwan, Ibrahim Alhamrouni, Ateeq Ur Rehman, et al. "An Optimized Solution for Fault Detection and Location in Underground Cables Based on Traveling Waves." Energies 15, no. 17 (2022): 6468. http://dx.doi.org/10.3390/en15176468.

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Faults in the power system affect the reliability, safety, and stability. Power-distribution systems are familiar with the different faults that can damage the overall performance of the entire system, from which they need to be effectively cleared. Underground power systems are more complex and require extra accuracy in fault detection and location for optimum fault management. Slow processing and the unavailability of a protection zone for relay coordination are concerns in fault detection and location, as these reduce the performance of power-protection systems. In this regard, this article

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Jain, Anamika. "Artificial Neural Network-Based Fault Distance Locator for Double-Circuit Transmission Lines." Advances in Artificial Intelligence 2013 (February 7, 2013): 1–12. http://dx.doi.org/10.1155/2013/271865.

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This paper analyses two different approaches of fault distance location in a double circuit transmission lines, using artificial neural networks. The single and modular artificial neural networks were developed for determining the fault distance location under varying types of faults in both the circuits. The proposed method uses the voltages and currents signals available at only the local end of the line. The model of the example power system is developed using Matlab/Simulink software. Effects of variations in power system parameters, for example, fault inception angle, CT saturation, sourc

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Ma, Junqing, Xingxing Jiang, Baokun Han, Jinrui Wang, Zongzhen Zhang, and Huaiqian Bao. "Dynamic Simulation Model-Driven Fault Diagnosis Method for Bearing under Missing Fault-Type Samples." Applied Sciences 13, no. 5 (2023): 2857. http://dx.doi.org/10.3390/app13052857.

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Existing generative adversarial networks (GAN) have potential in data augmentation and in the intelligent fault diagnosis of bearings. However, most relevant studies only focus on the fault diagnosis of rotating machines with sufficient fault-type samples, and some rare fault-type samples may be missing in training in practical engineering. To address those deficiencies, this paper presents an intelligent fault diagnosis method based on the dynamic simulation model and Wasserstein generative adversarial network with gradient normalization (WGAN-GN). The dynamic simulation model of bearing faul

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Wei, Wang, Kang Ruiqing, and Zhang Yu. "Overtemperature fault diagnosis of front bearing for main spindle based on CNN + LSTM." Journal of Physics: Conference Series 2295, no. 1 (2022): 012004. http://dx.doi.org/10.1088/1742-6596/2295/1/012004.

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Abstract The main spindle is an important transmission component of the wind turbine. The overtemperature fault of the front bearing of the main spindle is caused due to mechanical wear, grease failure and other reasons. A neural network based on convolutional neural networks (CNN) and long short memory network is built (LSTM) to judge the early fault. Method used in this paper can find the fault in advance. Compared with BP neural network, support vector machine, the accuracy of the model used in this paper is higher, which is up to 99.77%. The mechanism model of spindle operation will be est

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Tsioumpri, Eleni, Bruce Stephen, and Stephen D. J. McArthur. "Weather Related Fault Prediction in Minimally Monitored Distribution Networks." Energies 14, no. 8 (2021): 2053. http://dx.doi.org/10.3390/en14082053.

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Power distribution networks are increasingly challenged by ageing plant, environmental extremes and previously unforeseen operational factors. The combination of high loading and weather conditions is responsible for large numbers of recurring faults in legacy plants which have an impact on service quality. Owing to their scale and dispersed nature, it is prohibitively expensive to intensively monitor distribution networks to capture the electrical context these disruptions occur in, making it difficult to forestall recurring faults. In this paper, localised weather data are shown to support f

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Zheng, Wei, Desheng Hu, and Jing Wang. "Fault Localization Analysis Based on Deep Neural Network." Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/1820454.

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With software’s increasing scale and complexity, software failure is inevitable. To date, although many kinds of software fault localization methods have been proposed and have had respective achievements, they also have limitations. In particular, for fault localization techniques based on machine learning, the models available in literatures are all shallow architecture algorithms. Having shortcomings like the restricted ability to express complex functions under limited amount of sample data and restricted generalization ability for intricate problems, the faults cannot be analyzed accurate

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Zhang, Hui, Baojun Ge, and Bin Han. "Real-Time Motor Fault Diagnosis Based on TCN and Attention." Machines 10, no. 4 (2022): 249. http://dx.doi.org/10.3390/machines10040249.

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Motor failure can result in damage to resources and property. Real-time motor fault diagnosis technology can detect faults and diagnosis in time to prevent serious consequences caused by the continued operation of the machine. Neural network models can easily and accurately fault diagnose from vibration signals. However, they cannot notice faults in time. In this study, a deep learning model based on a temporal convolutional network (TCN) and attention is proposed for real-time motor fault diagnosis. TCN can extract features from shorter vibration signal sequences to allow the system to detect

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Saidina Omar, Abdul Malek, Muhammad Khusairi Osman, Mohammad Nizam Ibrahim, Zakaria Hussain, and Ahmad Farid Abidin. "Fault classification on transmission line using LSTM network." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 231. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp231-238.

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Deep Learning has ignited great international attention in modern artificial intelligence techniques. The method has been widely applied in many power system applications and produced promising results. A few attempts have been made to classify fault on transmission lines using various deep learning methods. However, a type of deep learning called Long Short-Term Memory (LSTM) has not been reported in literature. Therefore, this paper presents fault classification on transmission line using LSTM network as a tool to classify different types of faults. In this study, a transmission line model w

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Zhan, Zhongqiang, Dingqian Yang, Jian Wang, Jian Hao, Jie Wang, and Zhijie Ge. "Transformer Fault Diagnosis Method Based on Neural Network and D-S Evidence Theory." Journal of Physics: Conference Series 2260, no. 1 (2022): 012002. http://dx.doi.org/10.1088/1742-6596/2260/1/012002.

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Abstract The BP neural network is used to calculate the basic fault probability distribution of the dissolved gas in the transformer oil and the core grounding online monitoring data, and the Dempster-Shafer evidence theory is used to fuse the multi-source information of the basic probability of various types of faults to obtain a transformer fault diagnosis model. Transformer samples are used to verify the model, and the support vector machine and convolutional neural network fault diagnosis models are compared, and it is concluded that the proposed method is better in terms of fault diagnosi

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Rahaman, Munshi Mostafijur, Prasun Ghosal, and Tuhin Subhra Das. "Latency, Throughput and Power Aware Adaptive NoC Routing on Orthogonal Convex Faulty Region." Journal of Circuits, Systems and Computers 28, no. 04 (2019): 1950055. http://dx.doi.org/10.1142/s0218126619500555.

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Reliability of a Network-on-Chip (NoC) relies vastly upon the efficiency of handling faults. Faults those lead to trouble during on-chip communication process are basically of two types namely soft and hard. Here, hard faults are considered. Hard faults may be caused due to failure of links, routers, or other processing units. These are mainly dealt with fault-tolerant routing algorithms or by employing redundant hardware. Multiple faulty nodes are being avoided by acquiring region-based approaches. Most of the fault-tolerant routing techniques are designed on homogeneous faulty regions where

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Trivedi, Mihir, Riya Kakkar, Rajesh Gupta, et al. "Blockchain and Deep Learning-Based Fault Detection Framework for Electric Vehicles." Mathematics 10, no. 19 (2022): 3626. http://dx.doi.org/10.3390/math10193626.

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The gradual transition from a traditional transportation system to an intelligent transportation system (ITS) has paved the way to preserve green environments in metro cities. Moreover, electric vehicles (EVs) seem to be beneficial choices for traveling purposes due to their low charging costs, low energy consumption, and reduced greenhouse gas emission. However, a single failure in an EV’s intrinsic components can worsen travel experiences due to poor charging infrastructure. As a result, we propose a deep learning and blockchain-based EV fault detection framework to identify various types of

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Khalil, Mohamed A., Arshad Ahmad, Tuan Amran T. Abdullah, and Ali Al-shanini. "Failure Analysis Using Functional Model and Bayesian Network." Chemical Product and Process Modeling 11, no. 4 (2016): 265–72. http://dx.doi.org/10.1515/cppm-2016-0007.

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Abstract A class of functional model known as multilevel flow model (MFM) is used to represent a pilot scale heat exchanger system. MFM is effective in representing chemical process qualitatively through graphical representation, but lacks the ability to quantify the impact of successes or failures of process events, and is not able to quantitatively distinguish between steps in a goal and their contributions towards achieving the main goal. To address this issue, the MFM is converted into its equivalent fault tree (FT) model to accommodate logical sequence of events along with the needed quan

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Li, He Jia, Yan Wei Cheng, Cheng Yao, Hai Feng Xu, Zhao Yao, and Chang Feng Qu. "Fault Diagnosis Method of Vehicle Power System Using Bayesian Network." Applied Mechanics and Materials 556-562 (May 2014): 3134–38. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3134.

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The fault diagnosis of vehicle power system that the structure and characteristics of components are complex, each module and internal modules exist coupling, cross-linked mutual relations and the uncertainties, the system status and working conditions are difficult to describe by precisely mathematical model, and test cost expensive, less fault samples. Thus its fault diagnosis is the decision problem of uncertain information in a small sample. it is proposed that combining multi-signal flow graph model with Bayesian network fault diagnosis method. The fault diagnosis model of power system an

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Lee, Jong-Hyun, Jae-Hyung Pack, and In-Soo Lee. "Fault Diagnosis of Induction Motor Using Convolutional Neural Network." Applied Sciences 9, no. 15 (2019): 2950. http://dx.doi.org/10.3390/app9152950.

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Induction motors are among the most important components of modern machinery and industrial equipment. Therefore, it is necessary to develop a fault diagnosis system that detects the operating conditions of and faults in induction motors early. This paper presents an induction motor fault diagnosis system based on a CNN (convolutional neural network) model. In the proposed method, vibration signal data are obtained from the induction motor experimental environment, and these values are input into the CNN. Then, the CNN performs fault diagnosis. In this study, fault diagnosis of an induction mo

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Shuai, Yang. "Research on Fault Diagnosis Technology of Industrial Robot Operation Based on Deep Belief Network." Scientific Programming 2022 (July 5, 2022): 1–12. http://dx.doi.org/10.1155/2022/9260992.

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Fault diagnosis technology is the science of identifying the operating state of a machine or unit, and it studies the response of the change in the operating state of the machine or unit in the diagnostic information. It can give an early warning to the failure state of the machine and stop the machine before a major failure occurs so as to protect the life safety of the on-site staff and avoid huge economic losses to the enterprise. For mechanical equipment, fault diagnosis consists of three main links: fault detection; fault identification; and fault classification. Aiming at the problems th

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Shan, Xianming, Huixin Liu, and Yefeng Liu. "Research on fault tolerant control system based on optimized neural network algorithm." Journal of Intelligent & Fuzzy Systems 39, no. 6 (2020): 9073–83. http://dx.doi.org/10.3233/jifs-189306.

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Due to the strict personnel control measures in COVID-19 epidemic, the control system cannot be maintained and managed manually. This puts forward higher requirements for the accuracy of its fault-tolerant performance. The control system plays an increasingly important role in the rapid development of industrial production. When the sensor in the system fails, the system will become unstable. Therefore, it is necessary to accurately and quickly diagnose the faults of the system sensors and maintain the system in time. This paper takes the control system as the object to carry out the fault dia

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Zubairi, J. A. "An Overview of Optical Network Bandwidth and Fault Management." IIUM Engineering Journal 7, no. 1 (2010): 47–69. http://dx.doi.org/10.31436/iiumej.v7i1.76.

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This paper discusses the optical network management issues and identifies potential areas for focused research. A general outline of the main components in optical network management is given and specific problems in GMPLS based model are explained. Later, protection and restoration issues are discussed in the broader context of fault management and the tools developed for fault detection are listed. Optical networks need efficient and reliable protection schemes that restore the communications quickly on the occurrence of faults without causing failure of real-time applications using the netw

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Liu, Jingjing, Chuanyang Liu, Yiquan Wu, Huajie Xu, and Zuo Sun. "An Improved Method Based on Deep Learning for Insulator Fault Detection in Diverse Aerial Images." Energies 14, no. 14 (2021): 4365. http://dx.doi.org/10.3390/en14144365.

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Insulators play a significant role in high-voltage transmission lines, and detecting insulator faults timely and accurately is important for the safe and stable operation of power grids. Since insulator faults are extremely small and the backgrounds of aerial images are complex, insulator fault detection is a challenging task for automatically inspecting transmission lines. In this paper, a method based on deep learning is proposed for insulator fault detection in diverse aerial images. Firstly, to provide sufficient insulator fault images for training, a novel insulator fault dataset named “I

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Yu, Jian Li, and Zhe Zhang. "Fault Diagnosis of Transformer Based on RBF Neural Network." Applied Mechanics and Materials 571-572 (June 2014): 201–4. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.201.

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According to the characteristics of fault types of the transformer ,RBF neural network is used to diagnose transformer fault. The paper regards six gases as inputs of the neural network and establishes RBF neural network model which can diagnose six transformer faults: low temperature overheat, medium temperature overheat, high temperature overheat, low energy discharge, high energy discharge and partial discharge . The Matlab simulation studies show that transformer fault diagnosis model based on RBF neural network diagnosis for failure beyond the traditional three-ratio method. The rate of t

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Li, Ye, Xiao Liu, Zhenliang Yang, et al. "Prediction Model for Geologically Complicated Fault Structure Based on Artificial Neural Network and Fuzzy Logic." Scientific Programming 2022 (March 10, 2022): 1–12. http://dx.doi.org/10.1155/2022/2630953.

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The development and distribution of geologically complicated fault structure have the characteristics of uncertainty, randomness, ambiguity, and variability. Therefore, the prediction of complicated fault structures is a typical nonlinear problem. Neither fuzzy logic method nor artificial neural network alone can solve this problem well because the fuzzy method is generally not easy to realize adaptive learning function, and the neural network method is not suitable for describing sedimentary microfacies or geophysical facies. Therefore, taking the marginal subsags in the Jiyang Depression, Ea

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Wang, Xu, Hongyang Gu, Tianyang Wang, Wei Zhang, Aihua Li, and Fulei Chu. "Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical fault diagnosis of bearings." Frontiers of Mechanical Engineering 16, no. 4 (2021): 814–28. http://dx.doi.org/10.1007/s11465-021-0650-6.

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AbstractThe fault diagnosis of bearings is crucial in ensuring the reliability of rotating machinery. Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspective due to the powerful ability in learning fault-related knowledge. However, the inexplicability and low generalization ability of fault diagnosis models still bar them from the application. To address this issue, this paper explores a decision-tree-structured neural network, that is, the deep convolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings. Th

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