Journal articles on the topic 'Network fault model'
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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 (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.
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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 (August 9, 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 estimate selections is proposed in order to eliminate undesired primary estimates. A recursive nonlinear least squares is applied in order to obtain a smooth estimate of the wheel radius. Two computer simulation examples using Matlab/Simulink show that the proposed method is very effective for incipient fault identification in the setting of both left and right wheel faults.
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Jha, Sudan, Sultan Ahmad, Deepak Prashar, Bashir Salah, Majid Bashir, Inam Ullah, and Nermin M. Salem. "A Proposed Waiting Time Algorithm for a Prediction and Prevention System of Traffic Accidents Using Smart Sensors." Electronics 11, no. 11 (June 2, 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 algorithms and algorithms for determining traffic accidents. It is used to estimate or predict traffic accident reliability as well. Typically, the type of fault data collected is the number of faults (the number of faults recorded during a given time interval) or the time of fault (the time-of-fault data recorded when each fault occurred), and this can be utilized only for group data types, rather than the time-of-fault data type.
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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 (May 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 symptoms (gas proportions). Using our model, it is possible to produce a list of the most likely faults and a list of the most informative gas analysis. Also, the proposed approach helps to eliminate the uncertainty that could exist, regarding the fault nature due to gases trapped in the transformer, or faults that result in more simultaneous gas percentages. The model accurately provides transformer fault diagnosis and prediction ability by calculating the probability of released gases. Furthermore, it predicts failures based on their relationships in the Bayesian network. Finally, we show how the approach works for five distinct electrical transformers of a power plant, by describing the advantages of having available a Bayesian network model based on the Duval triangle method for the fault prediction tasks.
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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 grounding short-circuit are obtained by the simulink model, and the signal is input to this network model. The local features of the cable fault signals are extracted using 1D-CNN and the fault signal timing information is captured using BiLSTM, which enables the diagnosis of cable faults based on the automatically extracted features. The experimental results of the simulation show that the model can obtain a good recognition performance and can achieve an overall accuracy of 99.45% in classifying the four short-circuit faults with 500 iterations. In addition, the analysis of loss function curves and accuracy curves shows that the method performs better than networks with only temporal feature extraction, such as 1D-CNN and LSTM.
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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 efficiently and more rapidly than the adding momentum BP neural network on the VSC-HVDC system faults diagnosing.
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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 implementation on computer, Using TWACS transmits the multiport data information, low cost and can achieve collapse go traffic. The simulation results of Matlab proved that the algorithm is effective.
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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 the key nodes from software network. A weighted software network model is built by using functions as nodes, call relationships as edges, and call times as weight. Exploiting recursive method, a fault probability metric FP of a function, is defined according to the fault accumulation characteristic, and a fault propagation capability metric FPC of a function is proposed according to the fault propagation characteristic. Based on the FP and FPC, the fault severity metric FS is put forward to obtain the function nodes with larger fault severity in software network. Experimental results on two real software networks show that the algorithm MFS_AN can discover the key function nodes correctly and effectively.
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Farsoni, Saverio, Silvio Simani, and Paolo Castaldi. "Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis." Applied Sciences 11, no. 11 (May 29, 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 measurements of the considered process and the faults. To this end, the considered prototypes are integrated with auto-regressive with exogenous input descriptions, thus making them able to approximate unknown nonlinear dynamic functions with arbitrary degree of accuracy. These residual generators are estimated from the input and output measurements acquired from a high-fidelity benchmark that simulates the healthy and the faulty behaviour of a wind turbine system. The robustness and the reliability features of the developed solutions are validated in the presence of model-reality mismatch and modelling error effects featured by the wind turbine simulator. Moreover, a hardware-in-the-loop tool is implemented for testing and comparing the performance of the developed fault diagnosis strategies in a more realistic environment and with respect to different fault diagnosis approaches. The achieved results have demonstrated the effectiveness of the developed schemes also with respect to more complex model-based and data-driven fault diagnosis methodologies.
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Robson, Stephen, Abderrahmane Haddad, and Huw Griffiths. "Traveling Wave Fault Location Using Layer Peeling." Energies 12, no. 1 (December 30, 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 with the classical “Schur” layer-peeling algorithm, this paper develops a new approach to map the reflection coefficients of an electrical network, then use this information post-fault to determine accurately and robustly the location of either permanent or incipient faults on overhead networks. The robustness of the method is derived from the similarity between the post-fault energy reaching the observation point and the predicted energy, which is based on real network observations rather than a simulation model. The method is shown to perform well for different noise levels and fault inception angles on the IEEE 13-bus network, indicating that the method is well suited to radial distribution networks.
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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 (June 11, 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‐slip rates from the Uniform California Earthquake Rupture Model v.3 (UCERF3) and geologic slip‐rate estimates from specific locations. The model results support continuous linkage from the SAF through the Brawley seismic zone to the Imperial and to the Cerro Prieto faults. Connected faults decrease surface strain rates throughout the region and match more slip‐rate data. Only one model reproduces the UCERF3 rate on the Imperial fault, reaching the lower bound of 15 mm/yr. None of the tested models reproduces the UCERF3 preferred rate of 35 mm/yr. In addition, high‐strain energy density rates around the Cerro Prieto fault in all models suggest that the UCERF3 preferred rate of 35 mm/yr may require revision. The Elmore Ranch fault‐slip rate matches the UCERF3 rate only in models with continuous linkage. No long‐term slip‐rate data are available for the El Centro and Dixieland faults, but all models return less than 2 mm/yr on the El Centro fault and 3.5–9.6 mm/yr on the Dixieland fault. This suggests that the Dixieland fault may accommodate a significant portion of plate‐boundary motion.
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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 (December 10, 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 efficient operation. This increases computational complexity, storage cost & end-to-end processing delay of the network, which reduces its QoS performance under real-time use cases. To mitigate these issues, this text proposes design of a hybrid bioinspired model for fault-tolerant energy harvesting networks via fuzzy rule checks. The proposed model initially uses a Genetic Algorithm (GA) to cluster nodes depending upon their residual energy & distance metrics. Clustered nodes are processed via Particle Swarm Optimization (PSO) that assists in deploying a fault-tolerant & energy-harvesting process. The PSO model is further augmented via use of a hybrid Ant Colony Optimization (ACO) Model with Teacher Learner Based Optimization (TLBO), which assists in value-based fault prediction & mitigation operations. All bioinspired models are trained-once during initial network deployment, and then evaluated subsequently for each communication request. After a pre-set number of communications are done, the model re-evaluates average QoS performance, and incrementally reconfigures selected solutions. Due to this incremental tuning, the model is observed to consume lower energy, and showcases lower complexity when compared with other state-of-the-art models. Upon evaluation it was observed that the proposed model showcases 15.4% lower energy consumption, 8.5% faster communication response, 9.2% better throughput, and 1.5% better packet delivery ratio (PDR), when compared with recently proposed energy harvesting models. The proposed model also showcased better fault prediction & mitigation performance when compared with its counterparts, thereby making it useful for a wide variety of real-time network deployments.
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Zhang, Chunhua, Wen Fang, Baopeng Zhao, Zhen Xie, Changning Hu, Hongzhuan Wen, and Tao Zhong. "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 + fault points are obtained according to the five-year fault dataset of FAW Volkswagen Reck system, and the relevant conditional probabilities are determined by fuzzy set theory due to the lack of data and the uncertainty in expert scoring. Finally, the relevant fault point probability values are determined according to the Bayesian network inference algorithm in the case of single or parallel UB + fault sign occurring, and the fault diagnosis sequence is guided, further improving fault diagnosis efficiency.
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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 (January 31, 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-based fault detection approach for DL programs, using meta-modeling and graph transformations. First, we design a meta-model for DL programs that includes their base skeleton and fundamental properties. Then, we construct a graph-based verification process that covers 23 rules defined on top of the meta-model and implemented as graph transformations to detect faults and design inefficiencies in the generated models (i.e., instances of the meta-model). First, the proposed approach is evaluated by finding faults and design inefficiencies in 28 synthesized examples built from common problems reported in the literature. Then NeuraLint successfully finds 64 faults and design inefficiencies in 34 real-world DL programs extracted from Stack Overflow posts and GitHub repositories. The results show that NeuraLint effectively detects faults and design issues in both synthesized and real-world examples with a recall of 70.5% and a precision of 100%. Although the proposed meta-model is designed for feedforward neural networks, it can be extended to support other neural network architectures such as recurrent neural networks. Researchers can also expand our set of verification rules to cover more types of issues in DL programs.
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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 (February 28, 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 objective by doing a multi-horizon time series prediction of Bit Error Rate at the receiver end of an optical circuit using our custom designed Frequency aware Sequence to Sequence (FaS2S) Neural Network. The predicted value of BER can be used to notify users of failure scenarios before they occur. Further corrective action, such as automatic re-routing or manual intervention can then be taken by the user. With this model, we can even configure the network properties dynamically during periods of low BER to push the network efficiency to its maximum capacity. See inference Video for BER inference capabilities of FaS2S.
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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 (September 1, 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 neural network. MBNN successfully does the task of providing analytical redundancy for the aircraft sensor. In this work, both detection and reconfiguration of a fault is done using neural networks. Hence, the control system becomes robust for handling sensor failures near steady state and reconfiguration is also faster. A generalized regression neural network (GRNN), which is a type of radial basis network, is used for MBNN, which gives very efficient results for function approximation. An F8 aircraft model and C-Star controller, which improves its handling quality, are used for validation of the method involved. Models of F8 aircraft, C-Star controller, KBNNFD, and MBNN were developed using MATLAB/Simulink. Successful implementation and simulation results are shown and discussed using Simulink.
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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 (April 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 exist a pair of nonadjacent vertices in a 4-cycle whose degrees are both two after faulty edges are removed.
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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 (October 2, 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 symmetrical normal motor and demagnetization faulty motor models, as well as an asymmetrical eccentric fault model; conduct a joint simulation with MATLAB-Simulink to obtain fault current data; convert the collected current data into grayscale images, using the data set expansion method to form training and test data sets; and improve the convolutional neural network (CNN) network structure, that is, adding jump connections after each pooling layer and adding a spatial pyramid pooling layer after the last pooling layer to form a new CNN structure. Experimental results show that the new CNN can extract different levels and different scales of motor fault features hidden in the image, and can effectively diagnose different types of IPMSM faults. Compared with the traditional CNN, the new CNN has a higher fault diagnosis accuracy, up to 98.16%, 2.3% higher.
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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 solution of two models are solved by CCE algorithm, then fault element (s) in transmission network and fault section (s) in distribution network can be identified by the optimal solution. Various fault conditions are simulated in test system and the results show that conclusions obtained by proposed method are correct, which prove CCE algorithm diagnose fault effectively.
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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 (January 18, 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 proposed method, ANN uses optimum features obtained from the voltage, current, and their derivative signals. These features cannot be accurate to use in ANN because they cannot give reliable accuracy results. For this reason, first, the wavelet transform applies to the fault and non-fault signals to remove the noise. Then the ACO reduces unimportant features from the feature vector. Finally, the optimum features are used in the training of ANN as faulty and non-faulty signals. The multi-layer perceptron used in the suggested method consists of many layers, enabling the creation of a probability reconstruction over the inputs by the model. A supervised learning method is used to train each layer based on the selected features obtained from the ant colony optimization-discrete wavelet transform metaheuristic method. The artificial neural network technique is used to fine-tune the model to reduce the difference between true and anticipated classes’ error. The input signal and sampling frequencies are changed to examine the suggested strategy’s effectiveness. The obtained results demonstrate that the suggested fault detection and classification model can accurately diagnose HVDC faults. A comparison of the Support vector machine, Decision Tree, K-nearest neighbor algorithm (K-NN), and Ensemble classifier Machine techniques is made to verify the suggested method’s unquestionably higher performance.
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Tariq, Rizwan, Ibrahim Alhamrouni, Ateeq Ur Rehman, Elsayed Tag Eldin, Muhammad Shafiq, Nivin A. Ghamry, and Habib Hamam. "An Optimized Solution for Fault Detection and Location in Underground Cables Based on Traveling Waves." Energies 15, no. 17 (September 5, 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 proposes an optimized solution for a fault detection and location framework for underground cables based on a discrete wavelet transform (DWT). The proposed model supports area detection, the identification of faulty sections, and fault location. To overcome the abovementioned facts, we optimize the relay coordination for the overcurrent and timing relays. The proposed protection zone has two sequential stages for the current and time at which it optimizes the current and time settings of the connected relays through Newton–Raphson analysis (NRA). Moreover, the traveling times for the DWT are modeled, which relate to the protection zone provided by the relay coordination, and the faulty line that is identified as the relay protection is not overlapped. The model was tested for 132 kV/11 kV and 16-node networks for underground cables, and the obtained results show that the proposed model can detect and locate the cable’s faults speedily, as it detects the fault in 0.01 s, and at the accurate location. MATLAB/Simulink (DigSILENT Toolbox) is used to establish the underground network for fault location and detection.
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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, source strength, its X/R ratios, fault resistance, fault type and distance to fault have been investigated extensively on the performance of the neural network based protection scheme (for all ten faults in both the circuits). Additionally, the effects of network changes: namely, double circuit operation and single circuit operation, have also been considered. Thus, the present work considers the entire range of possible operating conditions, which has not been reported earlier. The comparative results of single and modular neural network indicate that the modular approach gives correct fault location with better accuracy. It is adaptive to variation in power system parameters, network changes and works successfully under a variety of operating conditions.
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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 (February 23, 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 faults is constructed to obtaining simulation signals to replace and complement the missing fault samples, which are combined with the measured signals as training data and then input into the proposed WGAN-GN model for expanding and enhancing the data. To test the effectiveness of the simulated samples, a fault classification model constructed by stacked autoencoders (SAE) is used to classify the enhanced dataset. According to the results, the proposed model performs well when used to diagnose faults under missing samples and is preferable to other methods.
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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 (June 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 established to analyse the manifestations of various faults and improve the accuracy in the future.
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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 (April 7, 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 fault prediction on distribution networks. Operational data are temporally aligned with meteorological observations to identify recurring fault causes with the potentially complex relation between them learned from historical fault records. Five years of data from a UK Distribution Network Operator is used to demonstrate the approach at both HV and LV distribution network levels with results showing the ability to predict the occurrence of a weather related fault at a given substation considering only meteorological observations. Unifying a diverse range of previously identified fault relations in a single ensemble model and accompanying the predicted network conditions with an uncertainty measure would allow a network operator to manage their network more effectively in the long term and take evasive action for imminent events over shorter timescales.
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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 accurately via those methods. To that end, we propose a fault localization method based on deep neural network (DNN). This approach is capable of achieving the complex function approximation and attaining distributed representation for input data by learning a deep nonlinear network structure. It also shows a strong capability of learning representation from a small sized training dataset. Our DNN-based model is trained utilizing the coverage data and the results of test cases as input and we further locate the faults by testing the trained model using the virtual test suite. This paper conducts experiments on the Siemens suite and Space program. The results demonstrate that our DNN-based fault localization technique outperforms other fault localization methods like BPNN, Tarantula, and so forth.
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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 (March 30, 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 and diagnose faults faster. In addition, attention allows the model to have higher diagnostic accuracy. The experiments demonstrate that the proposed model is able to detect faults in time when they occur and has an excellent diagnostic accuracy.
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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 (October 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 with 400 kV and 100 km distance was modelled. Fault free and 10 types of fault signals are generated from the transmission line model. Fault signals are pre-processed by extracting post-fault current signals. Then, these signals are fed as input to the LSTM network and trained to classify 10 types of faults. The white Gaussian noise of level 20 dB and 30 dB signal to noise ratio (SNR) is also added to the fault current signals to evaluate the immunity of the proposed model. Simulation results show promising classification accuracy of 100%, 99.77% and 99.55% for ideal, 30 dB and 20 dB noise respectively. Results has been compared to four different methods which can be seen that the LSTM leading with the highest classification accuracy. In line with the purpose of the LSTM functions, it can be concluded that the method has a capability to classify fault signals with high accuracy.
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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 (April 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 diagnosis accuracy and stability.
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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 (March 31, 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 some active nodes also act as deactivated nodes to build the region homogeneous. On the other hand, adaptive routing on nonhomogeneous faulty regions increases load on its boundary and most of them does not assure deadlock freeness. This paper proposes a deadlock-free adaptive fault-tolerant NoC routing named F-Route-NoC-Mesh (FRNM) ignoring any virtual channel on orthogonal convex faulty regions. Contributions of this work focus on balancing network traffic by assuming a virtual faulty block boundary and routing packets through this virtual boundary. Destination does not exist within that virtual faulty block regions to reduce load on the boundary of orthogonal faulty regions. Thus, this work is aimed at acquiring proper incorporation of procedures being able to reach fault-tolerant degree, routing efficiency and performance enhancement. Using the proposed algorithm (FRNM), a fault block model-based approach is developed. Significant improvements of average latency (43.37% to 60.44%), average throughput (4.18% to 90.81%) and power consumption (5.93% to 33.28%) are achieved over the state-of-the-art by using a cycle accurate simulator.
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Trivedi, Mihir, Riya Kakkar, Rajesh Gupta, Smita Agrawal, Sudeep Tanwar, Violeta-Carolina Niculescu, Maria Simona Raboaca, Fayez Alqahtani, Aldosary Saad, and Amr Tolba. "Blockchain and Deep Learning-Based Fault Detection Framework for Electric Vehicles." Mathematics 10, no. 19 (October 4, 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 faults, such as air tire pressure, temperature, and battery faults in vehicles. Furthermore, we employed a 5G wireless network with an interplanetary file system (IPFS) protocol to execute the fault detection data transactions with high scalability and reliability for EVs. Initially, we utilized a convolutional neural network (CNN) and a long-short term memory (LSTM) model to deal with air tire pressure fault, anomaly detection for temperature fault, and battery fault detection for EVs to predict the presence of faulty data, which ensure safer journeys for users. Furthermore, the incorporated IPFS and blockchain network ensure highly secure, cost-efficient, and reliable EV fault detection. Finally, the performance evaluation for EV fault detection has been simulated, considering several performance metrics, such as accuracy, loss, and the state-of-health (SoH) prediction curve for various types of identified faults. The simulation results of EV fault detection have been estimated at an accuracy of 70% for air tire pressure fault, anomaly detection of the temperature fault, and battery fault detection, with R2 scores of 0.874 and 0.9375.
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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 (December 1, 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 quantifications. The FT model is then converted into Bayesian network (BN) model to facilitate updates of probabilities. Using Hugin 8.1 software, the BN model is simulated to investigate the response of the process when subjected to various faults. The results highlight the capability of the model in detecting process faults and in identifying the associated root causes, thus pointing to the potentials of the proposed strategy in modeling complex chemical processes for higher level functions in plant operations such as facilitating alarm system and fault diagnosis.
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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 and the corresponding Bayesian network structure are built, which achieve the fault diagnosis of power system, Diagnosis example shows that the method of the vehicle power has a higher failure troubleshooting capabilities of the system single and multiple faults.
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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 (July 24, 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 motor is performed in three states, namely, normal, rotor fault, and bearing fault. In addition, a GUI (graphical user interface) for the proposed fault diagnosis system is presented. The experimental results confirm that the proposed method is suitable for diagnosing rotor and bearing faults of induction motors.
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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 that need to be solved in the fault diagnosis of industrial robots, this paper adopts a data-driven intelligent diagnosis method to establish a fault diagnosis model of industrial robots based on Deep Belief Network (DBN) and DSmT theory. Firstly, based on wavelet transform and information energy entropy correlation theory, the vibration signal of industrial robot is extracted, and the energy entropy normalized eigenvector is established. Then, the energy entropy normalized feature vector is divided into training set and test set to complete the creation of DBN network model. Finally, using DSmT theory to carry out decision-making fusion, a fault diagnosis model for industrial robots is established, and experiments are carried out on the K-R-R540 robot to verify the applicability of the established fault diagnosis model. It is proved by experiments that the industrial robot fault diagnosis model based on the deep belief network can meet the requirements of the recognition accuracy of robot faults, and the model will perform poorly when the faults coexist with multiple faults.
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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 (December 4, 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 diagnosis and fault-tolerant control research of its sensors. A network model of wavelet neural network is proposed, and an improved genetic algorithm is used to optimize the weights and thresholds of the neural network model to avoid the deficiencies of traditional neural network algorithms. For the depth sensor of a certain system, an online fault diagnosis scheme based on RBF (Radial Basis Function) neural network and genetic algorithm optimized neural network was designed. The disturbance fault, “stuck” fault, drift fault and oscillation fault of the depth sensor are simulated. Simulation experiments show that both online fault diagnosis schemes can accurately identify sensor faults and the genetic algorithm optimized neural network is superior to RBF neural network in both recognition accuracy and training time under the influence of COVID-19.
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Zubairi, J. A. "An Overview of Optical Network Bandwidth and Fault Management." IIUM Engineering Journal 7, no. 1 (September 29, 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 network. A holistic approach is required that provides mechanisms for fault detection, rapid restoration and reversion in case of fault resolution. Since the role of SDH/SONET is diminishing, the modern optical networks are poised towards the IP-centric model where high performance IP-MPLS routers manage a core intelligent network of IP over WDM. Fault management schemes are developed for both the IP layer and the WDM layer. Faults can be detected and repaired locally and also through centralized network controller. A hybrid approach works best in detecting the faults where the domain controller verifies the established LSPs in addition to the link tests at the node level. On detecting a fault, rapid restoration can perform localized routing of traffic away from the affected port and link. The traffic may be directed to pre-assigned backup paths that are established as shared or dedicated resources. We examine the protection issues in detail including the choice of layer for protection, implementing protection or restoration, backup path routing, backup resource efficiency, subpath protection, QoS traffic survival and multilayer protection triggers and alarm propagation. The complete protection cycle is described and mechanisms incorporated into RSVP-TE and other protocols for detecting and recording path errors are outlined. In addition, MPLS testbed configuration procedure is outlined with suggested topologies. Open issues in this area are identified and current work is highlighted. It is expected that this paper will serve as a catalyst to accelerate the research and development activities in high speed networking.
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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 (July 20, 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 “InSF-detection” is constructed. Secondly, an improved YOLOv3 model is proposed to reuse features and prevent feature loss. To improve the accuracy of insulator fault detection, SPP-networks and a multi-scale prediction network are employed for the improved YOLOv3 model. Finally, the improved YOLOv3 model and the compared models are trained and tested on the “InSF-detection”. The average precision (AP) of the improved YOLOv3 model is superior to YOLOv3 and YOLOv3-dense models, and just a little (1.2%) lower than that of CSPD-YOLO model; more importantly, the memory usage of the improved YOLOv3 model is 225 MB, which is the smallest between the four compared models. The experimental results and analysis validate that the improved YOLOv3 model achieves good performance for insulator fault detection in aerial images with diverse backgrounds.
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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 the transformer fault diagnosis accuracy reaches 91.67% which is also much higher than the traditional three ratio method.
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Li, Ye, Xiao Liu, Zhenliang Yang, Chao Zhang, Mingchun Song, Zhaolu Zhang, Shiyong Li, and Weiqiang Zhang. "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.
Full textAbstract:
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, Eastern China, as a study case, this paper uses the method of combining artificial neural network and fuzzy logic to study geologically complicated fault structure prediction model. This paper expounds on the research status and significance of geologically complicated fault structure prediction model, elaborates the development background, current status, and future challenges of artificial neural networks and fuzzy logic, introduces the method and principle of fuzzy neural network structure and fuzzy logic analysis algorithm, conducts prediction model design and implementation based on fuzzy neural network, proposes the learning algorithm of fuzzy neural network, analyzes the programming realization of fuzzy neural network, constructs complicated fault structure prediction model based on the artificial neural network and fuzzy logic, performs the fuzzy logic system selection of complicated fault structure prediction model, carries out the artificial neural network structure design of complicated fault structure prediction model, compares the prediction effects of the geologically complicated fault structure model based on artificial neural networks and fuzzy logic, and finally discusses the system design and optimization of the prediction model for geologically complicated fault structures. The study results show that the fuzzy neural network fully integrates the advantages of artificial neural network and fuzzy logic system; based on the clear physical background of fuzzy logic system, it effectively integrates powerful knowledge expression ability and fuzzy reasoning ability into the network knowledge structure of neural network, which greatly improves the prediction accuracy of geologically complicated fault structure.
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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 (October 28, 2021): 814–28. http://dx.doi.org/10.1007/s11465-021-0650-6.
Full textAbstract:
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. The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision tree methods by rebuilding the output decision layer of CNN according to the hierarchical structural characteristics of the decision tree, which is by no means a simple combination of the two models. The proposed DCTN model has unique advantages in 1) the hierarchical structure that can support more accuracy and comprehensive fault diagnosis, 2) the better interpretability of the model output with hierarchical decision making, and 3) more powerful generalization capabilities for the samples across fault severities. The multiclass fault diagnosis case and cross-severity fault diagnosis case are executed on a multicondition aeronautical bearing test rig. Experimental results can fully demonstrate the feasibility and superiority of the proposed method.