Thematische Bibliographien / FAULTY NODE DETECTION

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Auswahl der wissenschaftlichen Literatur zum Thema „FAULTY NODE DETECTION“

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Veröffentlicht am 16. Januar 2024

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Zeitschriftenartikel zum Thema "FAULTY NODE DETECTION"

Panda, Meenakshi, und P. M. Khilar. „Energy Efficient Distributed Fault Identification Algorithm in Wireless Sensor Networks“. Journal of Computer Networks and Communications 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/323754.

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A distributed fault identification algorithm is proposed here to find both hard and soft faulty sensor nodes present in wireless sensor networks. The algorithm is distributed, self-detectable, and can detect the most common byzantine faults such as stuck at zero, stuck at one, and random data. In the proposed approach, each sensor node gathered the observed data from the neighbors and computed the mean to check whether faulty sensor node is present or not. If a node found the presence of faulty sensor node, then compares observed data with the data of the neighbors and predict probable fault status. The final fault status is determined by diffusing the fault information from the neighbors. The accuracy and completeness of the algorithm are verified with the help of statistical model of the sensors data. The performance is evaluated in terms of detection accuracy, false alarm rate, detection latency and message complexity.

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Bae, Jangsik, Meonghun Lee und Changsun Shin. „A Data-Based Fault-Detection Model for Wireless Sensor Networks“. Sustainability 11, Nr. 21 (05.11.2019): 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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Mahapatro, Arunanshu, und Pabitra Mohan Khilar. „An Adaptive Approach to Discriminate the Persistence of Faults in Wireless Sensor Networks“. ISRN Sensor Networks 2012 (14.10.2012): 1–13. http://dx.doi.org/10.5402/2012/342461.

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This paper presents a parametric fault detection algorithm which can discriminate the persistence (permanent, intermittent, and transient) of faults in wireless sensor networks. The main characteristics of these faults are the amount the fault appears. We adopt this state-holding time to discriminate transient from intermittent faults. Neighbor-coordination-based approach is adopted, where faulty sensor nodes are detected based on comparisons between neighboring nodes and dissemination of the decision made at each node. Simulation results demonstrate the robustness of the work at varying transient fault rate.

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Xu, Xiaowei, Fangrong Zhou, Yongjie Nie, Wenhua Xu, Ke Wang, Jian OuYang, Kaihong Zhou, Shan Chen und Yiming Han. „Fault Detection and Location of 35 kV Single-Ended Radial Distribution Network Based on Traveling Wave Detection Method“. Processes 11, Nr. 8 (19.08.2023): 2494. http://dx.doi.org/10.3390/pr11082494.

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With the progress of society and the iterative improvement of infrastructure construction, the power grid transmission lines have also entered an era of intelligence. The national distribution system has made ensuring the regular operation of the distribution network as well as prompting troubleshooting and detection its top priority. Research on fault diagnosis for 35 kV single-ended radial distribution networks is still in its infancy compared to other hot topics in the industry, such as short-circuit fault detection and fault node localization. This study adopts the 35 kV single-ended radial distribution network as a model, detects fault lines via the traveling wave method, and accurately locates fault nodes using the wavelet conversion method, hoping to quickly identify and locate fault nodes in distribution networks. The experimental results demonstrate that the research method can quickly identify the faulty line and carry out further fault node location detection. The final obtained fault distance is 1.19 km with an actual error of only 0.16 km; the maximum relative errors are only 0.33 km and 0.21 km when the initial phase angle and transition resistance parameters are changed, respectively; and the error amplitude fluctuations are essentially stable. The experimental results also demonstrate that the research method can quickly identify the faulty line and carry out further fault node location.

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Islampurkar, Mangesh, Kishanprasad Gunale, Sunil Somani und Nikhil Bagade. „Multiple Stuck At Fault Diagnosis System For Digital Circuit On FPGA Using Vedic Multiplier and ANN“. International Journal of Circuits, Systems and Signal Processing 16 (30.05.2022): 985–92. http://dx.doi.org/10.46300/9106.2022.16.120.

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In an electronics circuit, the presence of a Fault leads to undesired or unexpected results. The output of many nodes on the circuit is changed due to the presence of the Fault at one node. So, it is necessary to detect the nature of the Fault present in a particular faulty node. To detect the fault present in the digital circuit, it is necessary to understand logical behavior using mathematical modeling. After the successful modeling, parameters are extracted, and the database is generated. The mathematical model uses Hebbian Artificial Neural Network algorithms [1] [2]. The database generated is used by the fault detection system to find the masked and multiple faults. A fault detection system monitors the faults present in the test circuit and finds the origin and nature of the Fault [3] [4]. The database generated for single stuck-at faults is used to find the multiple faults present in the faulty circuit. In this paper, Modified Vedic Multiplication [5] [4] method is used to optimize the utilization of the proposed system. In this proposed design multiplier of {N x N} bit input and {N} bit output is used, due to which device utilization is decreased, which is the expected outcome from the design. This system is designed using ISE Design Suite and implemented on Spartan-6 FPGA [6] [7].

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Sun, Yin Qiu, und Hai Lin Feng. „Intermittent Faults Diagnosis in Wireless Sensor Networks“. Applied Mechanics and Materials 160 (März 2012): 318–22. http://dx.doi.org/10.4028/www.scientific.net/amm.160.318.

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Sensor node intermittent faults which sometimes behave as fault-free are common in wireless sensor networks. Intermittent faults also affect network performance and faults detection accuracy, so it is important to diagnose the intermittent faulty nodes accurately. This paper proposes a distributed clustering intermittent faults diagnosis method. First, the network is divided into several clusters with the cluster heads should be diagnosed as good. Then, the cluster members are diagnosed by their cluster head. In order to improve the validity of proposed diagnose method, a strategy which collect data for many times is adopted. Analysis of fault diagnosable is given, and simulation results indicate the proposed algorithm has high fault detection accuracy.

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Liu, Kezhong, Yang Zhuang, Zhibo Wang und Jie Ma. „Spatiotemporal Correlation Based Fault-Tolerant Event Detection in Wireless Sensor Networks“. International Journal of Distributed Sensor Networks 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/643570.

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Reliable event detection is one of the most important objectives in wireless sensor networks (WSNs), especially in the presence of faulty nodes. Existing fault-tolerant event detection approaches usually take the probability of faulty nodes into account and fusion techniques to weaken the influence of faulty readings are usually developed. Through extensive experiments, we discover a phenomenon that event detection accuracy degrades quickly when the faulty sensors ratio reaches a critical value. This problem has not drawn enough attention and a solution to the problem is our concern. In this paper, a spatiotemporal correlation based fault-tolerant event detection scheme (STFTED) is proposed, which leverages a two-stage decision fusion and spatiotemporal correlation to improve the event detection quality. In the low-level local stage, a location-based weighted voting scheme (LWVS) is developed to make decision fusion locally on each sensor node, which is based on neighboring nodes and the geographical distributions of two decision quorums. In the high-level global stage, a Bayesian fusion algorithm is adopted to reach a consensus among individual detection decisions made by sensor nodes. Simulation results demonstrate that the proposed approach is highly effective and a better quality of event detection can be obtained compared with the optimal threshold decision schemes (OTDS).

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Saihi, Marwa, Ahmed Zouinkhi, Boumedyen Boussaid, Mohamed Naceur Abdelkarim und Guillaume Andrieux. „Hidden Gaussian Markov model for distributed fault detection in wireless sensor networks“. Transactions of the Institute of Measurement and Control 40, Nr. 6 (15.03.2017): 1788–98. http://dx.doi.org/10.1177/0142331217691334.

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Wireless sensor networks are based on a large number of sensor nodes used to measure information like temperature, acceleration, displacement, or pressure. The measurements are used to estimate the state of the monitored system or area. However, the quality of the measurements must be guaranteed to ensure the reliability of the estimated state of the system. Actually, sensors can be used in a hostile environment such as, on a battle field in the presence of fires, floods, earthquakes. In these environments as well as in normal operation, sensors can fail. The failure of sensor nodes can also be caused by other factors like: the failure of a module (such as the sensing module) due to the fabrication process models, loss of battery power and so on. A wireless sensor network must be able to identify faulty nodes. Therefore, we propose a probabilistic approach based on the Hidden Markov Model to identify faulty sensor nodes. Our proposed approach predicts the future state of each node from its actual state, so the fault could be detected before it occurs. We use an aided judgment of neighbour sensor nodes in the network. The algorithm analyses the correlation of the sensors’ data with respect to its neighbourhood. A systematic approach to divide a network on cliques is proposed to fully draw the neighbourhood of each node in the network. After drawing the neighbourhood of each node (cliques), damaged cliques are identified using the Gaussian distribution theorem. Finally, we use the Hidden Markov Model to identify faulty nodes in the identified damaged cliques by calculating the probability of each node to stay in its normal state. Simulation results demonstrate our algorithm is efficient even for a huge wireless sensor network unlike previous approaches.

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Duche, R. N., und N. P. Sarwade. „Faulty Sensor Node Detection Using Round Trip Time and Discrete Paths in WSNs“. ISRN Sensor Networks 2013 (23.09.2013): 1–12. http://dx.doi.org/10.1155/2013/941489.

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Wireless sensor networks (WSNs) with efficient and accurate design to increase the quality of service (QoS) have become a hot area of research. Implementing the efficient and accurate WSNs requires deployment of the large numbers of portable sensor nodes in the field. The quality of service of such networks is affected by lifetime and failure of sensor node. In order to improve the quality of service, the data from faulty sensor nodes has to be ignored or discarded in the decision-making process. Hence, detection of faulty sensor node is of prime importance. In the proposed method, discrete round trip paths (RTPs) are compared on the basis of round trip delay (RTD) time to detect the faulty sensor node. RTD protocol is implemented in NS2 software. WSNs with circular topology are simulated to determine the RTD time of discrete RTPs. Scalability of the proposed method is verified by simulating the WSNs with various sensor nodes.

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V, Bindhu, und Ranganathan G. „Effective Automatic Fault Detection in Transmission Lines by Hybrid Model of Authorization and Distance Calculation through Impedance Variation“. March 2021 3, Nr. 1 (27.03.2021): 36–48. http://dx.doi.org/10.36548/jei.2021.1.004.

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Fault detection in the transmission is a challenging task when examining the accuracy of the system. This fault can be caused by a man-made force or by using concurrent overvoltage in the power distribution line. This research focuses on two sections to handle the power transmission line problem and can be rectified as previously stated. An intelligent approach is utilized for monitoring and controlling line faults in order to improve the accuracy of the equipment in transmission line fault detection. After several iterations of the procedure, the combination of line and master unit improves the system's accuracy and reliability. The master unit identifies faulty poles in the network based on the variation of current and voltage of each node and calculates the distance between the station and the faulty node to reduce manual effort. In the proposed work, many sensors are used to detect the line fault in a network by placing the appropriate point. The pure information can be transferred to an authorized person or unit after many iterations due to knowledgeable devices. The faulty status of the pole information is displayed in the control unit by a display unit comprised of an alarm unit to alert the corresponding section using ZigBee techniques. The GSM unit provides the faulty status of an authorized person to rectify the problems immediately which further improve the reliability of the system. When compared to existing methods, our hybrid proposed method achieves a higher accuracy of 90%. This method aids to reduce the labor costs gradually to visit all-pole points instead of faulty pole points and thereby increasing the reliability of the electrical consumers.

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Dissertationen zum Thema "FAULTY NODE DETECTION"

Pettersson, Christopher. „Automatic fault detection and localization in IPnetworks : Active probing from a single node perspective“. Thesis, Linköpings universitet, Programvara och system, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-120414.

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Fault management is a continuously demanded function in any kind of network management. Commonly it is carried out by a centralized entity on the network which correlates collected information into likely diagnoses of the current system states. We survey the use of active-on-demand-measurement, often called active probes, together with passive readings from the perspective of one single node. The solution is confined to the node and is isolated from the surrounding environment. The utility for this approach, to fault diagnosis, was found to depend on the environment in which the specific node was located within. Conclusively, the less environment knowledge, the more useful this solution presents. Consequently this approach to fault diagnosis offers limited opportunities in the test environment. However, greater prospects was found for this approach while located in a heterogeneous customer environment.

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Ru, Jifeng. „Adaptive estimation and detection techniques with applications“. ScholarWorks@UNO, 2005. http://louisdl.louislibraries.org/u?/NOD,285.

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Thesis (Ph. D.)--University of New Orleans, 2005.
Title from electronic submission form. "A dissertation ... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Engineering and Applied Science"--Dissertation t.p. Vita. Includes bibliographical references.

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Lalem, Farid. „Cadre méthodologique et applicatif pour le développement de réseaux de capteurs fiables“. Thesis, Brest, 2017. http://www.theses.fr/2017BRES0063/document.

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Les réseaux de capteurs sans fil émergent comme une technologie innovatrice qui peut révolutionner et améliorer notre façon de vivre, de travailler et d'interagir avec l'environnement physique qui nous entoure. Néanmoins, l'utilisation d'une telle technologie soulève de nouveaux défis concernant le développement de systèmes fiables et sécurisés. Ces réseaux de capteurs sans fil sont souvent caractérisés par un déploiement dense et à grande échelle dans des environnements limités en terme de ressources. Les contraintes imposées sont la limitation des capacités de traitement, de stockage et surtout d'énergie car ils sont généralement alimentés par des piles.Nous visons comme objectif principal à travers cette thèse à proposer des solutions permettant de garantir un certain niveau de fiabilité dans un RCSF dédié aux applications sensibles. Nous avons ainsi abordé trois axes, qui sont :- Le développement de méthodes permettant de détecter les noeuds capteurs défaillants dans un RCSF,- Le développement de méthodes permettant de détecter les anomalies dans les mesures collectées par les nœuds capteurs, et par la suite, les capteurs usés (fournissant de fausses mesures).- Le développement de méthodes permettant d'assurer l'intégrité et l'authenticité des données transmise dans un RCSF
Wireless sensor networks emerge as an innovative technology that can revolutionize and improve our way to live, work and interact with the physical environment around us. Nevertheless, the use of such technology raises new challenges in the development of reliable and secure systems. These wireless sensor networks are often characterized by dense deployment on a large scale in resource-onstrained environments. The constraints imposed are the limitation of the processing, storage and especially energy capacities since they are generally powered by batteries.Our main objective is to propose solutions that guarantee a certain level of reliability in a WSN dedicated to sensitive applications. We have thus proposed three axes, which are:- The development of methods for detecting failed sensor nodes in a WSN.- The development of methods for detecting anomalies in measurements collected by sensor nodes, and subsequently fault sensors (providing false measurements).- The development of methods ensuring the integrity and authenticity of transmitted data over a WSN

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Frini, Marouane. „Diagnostic des engrenages à base des indicateurs géométriques des signaux électriques triphasés“. Thesis, Lyon, 2018. http://www.theses.fr/2018LYSES052.

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Bien qu’ils soient largement utilisés dans le domaine, les mesures vibratoires classiques présentent plusieurs limites. A la base, l’analyse vibratoire ne peut identifier qu’environ 60% des défauts qui peuvent survenir dans les machines. Cependant, les principaux inconvénients des mesures de la vibration sont l’accès difficile au système de transmission afin d’y placer le capteur ainsi que le coût conséquent de la mise en œuvre. Ceci résulte en des problèmes de sensibilité relatifs à la position de l’installation et ceux de difficulté pour distinguer la source de vibration à cause de la diversité des excitations mécaniques qui existent dans l’environnement industriel.Par conséquent, l’analyse des signatures du courant électrique des moteurs s’impose comme une alternative prometteuse à l’analyse vibratoire et a donc fait l’objet d’une attention grandissante au cours des dernières années. En effet, l’analyse des signatures électriques a l’avantage d’être une méthode techniquement accessible, non-intrusive au système et peu coûteuse. Les techniques basées sur le courant et la tension ne requièrent que les mesures électriques du moteur qui sont souvent déjà surveillées pour le contrôle et la protection des machines électriques. Ce processus a été principalement utilisé pour la détection des défauts de moteur tels que la rupture de barres du rotor et les défauts d’excentricité ainsi que les défauts de roulements. En revanche, très peu de recherches concernent la détection des défauts en utilisant l’analyse du courant. En outre, les signaux électriques triphasés sont caractérisés par des représentations géométriques particulières liées à leur forme d’onde qui peuvent servir en tant qu’indicateurs différents offrant des informations supplémentaires. Parmi ces indicateurs géométriques, les transformées de Park et de Concordia modélisent les composantes électriques dans un repère bidimensionnel et toute déviation par rapport à la représentation d’origine indique l’apparition d’un dysfonctionnement. Aussi, les équations différentielles de Frenet-Serret représentent la trajectoire du signal dans un espace euclidien tridimensionnel et indiquent ainsi tout changement dans l’état du système. Bien qu’ils aient été utilisés pour les défauts de roulements, ces indicateurs n’ont pas été appliqués dans la détection des défauts d’engrenages en utilisant l’analyse des signatures des courants électriques. D’où l’idée novatrice de combiner ces indicateurs avec des techniques de traitement de signal, ainsi que des techniques de classification pour le diagnostic des engrenages en utilisant l’analyse des signatures de courant et de tension du moteur électrique.Ainsi, dans ce travail, on propose une nouvelle approche pour le diagnostic des défauts d’engrenages en utilisant l’analyse des courants et des tensions électriques du stator de la machine et ceci en se basant sur un ensemble d’indicateurs géométriques (Transformées de Park et de Concordia ainsi que les propriétés du repère Frenet-Serret). Ces indicateurs font partie d’une bibliothèque de signatures de défauts qui a été construite et qui comprend également les indicateurs classiques utilisés pour un large éventail de défauts. Ainsi, un algorithme combine les acquisitions expérimentales des signaux électriques à des méthodes de traitement de signal avancées (décomposition modale empirique,…). Ensuite, celui-ci sélectionne les indicateurs les plus pertinents au sein de la bibliothèque en se basant sur les algorithmes de sélection de paramètres (sélection séquentielle rétrograde et analyse des composantes principales). Enfin, cette sélection est utilisée pour la classification non-supervisée (K-moyennes) pour la distinction entre l’état sain et l’état défaillant
Although they are widely used, classical vibration measurements have several limitations. Vibration analysis can only identify about 60% of the defects that may occur in mechanical systems. However, the main drawbacks of vibration measurements are the difficult access to the transmission system in order to place the sensor as well as the consequent cost of implementation. This results in sensitivity problems relative to the position of the installation and the difficulty to distinguish the source of vibration because of the diversity of mechanical excitations that exist in the industrial environment.Hence, the Motor Current Signatures Analysis (M.C.S.A.) represents a promising alternative to the vibration analysis and has therefore been the subject of increasing attention in recent years. Indeed, the analysis of electrical signatures has the advantage of being a technically accessible method as well as inexpensive and non-intrusive to the system. Techniques based on currents and voltages only require the motor’s electrical measurements which are often already supervised for the purposes of the control and the protection of the electrical machines. This process was mainly used for the detection of motors faults such as rotor bars breakage and eccentricity faults as well as bearings defects. On the other hand, very little research has been focused on gear faults detection using the current analysis. In addition, three-phase electrical signals are characterized by specific geometric representations related to their waveforms and they can serve as different indicators providing additional information. Among these geometric indicators, the Park and Concordia transforms model the electrical components in a two-dimensional coordinate system and any deviation from the original representation indicates the apparition of a malfunction. Moreover, the differential equations of Frenet-Serret represent the trajectory of the signal in a three-dimensional euclidean space and thus indicate any changes in the state of the system. Although they have been previously used for bearing defects, these indicators have not been applied in the detection of gear defects using the analysis of electrical current signatures. Hence, the innovative idea of combining these indicators with signal processing techniques, as well as classification techniques for gears diagnosis using the three-phase motor’s electrical current signatures analysis is established.Hence, in this work, a new approach is proposed for gear faults diagnosis using the motor currents analysis, based on a set of geometric indicators (Park and Concordia transforms as well as the properties of the Frenet-Serret frame). These indicators are part of a specifically built fault signatures library and which also includes the classical indicators used for a wide range of faults. Thus, a proposed estimation algorithm combines experimental measurements of electrical signals with advanced signal processing methods (Empirical Mode Decomposition, ...). Next, it selects the most relevant indicators within the library based on feature selection algorithms (Sequential Backward Selection and Principal Component Analysis). Finally, this selection is combined with non-supervised classification (K-means) for the distinction between the healthy state and faulty states. It was finally validated with a an additional experimental configuration in different cases with gear faults, bearing faults and combined faults with various load levels

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SHARMA, AKSHAY. „ANFIS AND FUZZY BASED FAULTY NODE DETECTION FOR WIRELESS SENSOR NETWORK“. Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/20398.

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One of the main challenges in WSN is detection and management of faulty nodes. Currently, the great majority of fault detection approaches rely on surrounding nodes' views of data, which ignores the happenings of the event or issues coverage. In this paper, we describe a novel distributed fuzzy logic-based defective node identification approach for heterogeneous WSNs. The algorithm, it is hypothesized, weights the observed data depending on criteria such as distance, coverage, and the difference between them. When the proposed distributed technique is employed, each sensor node can accurately estimate its own state even in the presence of events such as sudden and temporary failures. According to rigorous simulations, the proposed technique decreases false positives while also improving problem detection accuracy.

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Yang, Shih-An, und 楊世安. „A Log-Only Node Fault Detection Method in Wireless Sensor Network“. Thesis, 2011. http://ndltd.ncl.edu.tw/handle/40470148701113594855.

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Chen, Hsin-Hsiu, und 陳新秀. „An improved SPRT detection method for replication node in fault tolerant wireless sensor networks“. Thesis, 2017. http://ndltd.ncl.edu.tw/handle/3wq42g.

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碩士
國立交通大學
資訊管理研究所
105
As the Internet of Things came, the application of the wireless sensor networks has increased. Meanwhile, there are also many threats of networks security need to be dealt with. One of the network attacks is the replication attack. The attackers may replicate few of the nodes to be considered as the legitimate nodes. The cloned nodes would integrate into the original network and launch a variety of internal attacks. There are several replica detections in the literature for the mobile environment. Most of the detections are limited by high computation and communication cost. Some of detections based on the Sequential Probability Ratio Test have much lower system overhead. However, these prior works decrease the accuracy when sensors lie in a server environment so that sensors are prone to retransmit the message. This paper proposes a replica detection based on the SPRT in fault tolerant wireless sensor network. In order to improve the accuracy of the judgment, we use the power of nodes and the slope of energy as the appendix and apply the SPRT to adjust the replica detection dynamically in the fault tolerant environment. The experiments show that our proposed scheme achieves better performance on both efficiency of detecting and reduction of error rate than the prior work.

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(9780674), Esteban Bernal Arango. „Smart sensor node for freight wagon condition monitoring systems“. Thesis, 2021. https://figshare.com/articles/thesis/Smart_sensor_node_for_freight_wagon_condition_monitoring_systems/19184819.

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Unlike methods such as replacing components when they fail or on a calendar time schedule, Condition Based Maintenance (CBM) consists of quantifying component degradation in real-time, allowing repairs to be made only when necessary and improving the overall efficiency of mechanical systems. An ideal CBM application for freight railway wagons would consist of a wireless self-powered electronic device installed on each vehicle, detecting and communicating parameters such as brake, bearing or wheel faults, and dynamic instabilities. Such a monitoring device has not been achieved yet, mainly because of the lack of electricity on-board the vehicles and the cost of instrumenting massive fleets. Recent advances and cost reductions in devices and technologies for the Internet of Things (IoT) open the possibility for developing a feasible on-wagon monitoring device. Wireless data transmission, acquisition, and digital signal processing are the most power demanding tasks in on-board condition monitoring sensor nodes. Traditional approaches use mostly digital signal processing and opt for reducing monitoring and communication events, limiting the types of parameters that can be measured and the associated analyses. On the other hand, the approach of the present project is to develop an innovative hardware architecture based on analogue computing for decreasing energy consumption with a reduced sensor node architecture. That is, achieving fault detection using analogue electronic circuitry which directly extracts relevant information from sensor signals, hence reducing digital system workload and complexity and thereby being able to handle higher frequency analogue signals with simple electronic components. This project develops an innovative sensor node hardware architecture and algorithms for a practical on-wagon monitoring device, with low power usage and sufficient on-board calculation capability to provide warning messages when a fault emerges. Developments regarding sensors, IoT, integrated systems and fault detection techniques were reviewed. A wheel flat defect was used as a case study to develop and investigate the proposed condition monitoring sensor node. Railway vehicle dynamic behaviour was simulated to determine operating conditions for the device and the nature of the signals to be monitored. The device concept was firstly proven by combining vehicle dynamic simulations with a physical prototype of the on-wagon fault detection analogue circuit. Subsequently, a hardware prototype version of the circuit was constructed and tested on a scaled bogie rig. The proposed sensor node hardware architecture effectively reduced power consumption and memory requirements for detecting a wheel flat defect using on-board acceleration signals.

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Ali, Md Mohsin. „High Performance Fault-Tolerant Solution of PDEs using the Sparse Grid Combination Technique“. Phd thesis, 2016. http://hdl.handle.net/1885/109292.

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The data volume of Partial Differential Equation (PDE) based ultra-large-scale scientific simulations is increasing at a higher rate than that of the system’s processing power. To process the increased amount of simulation data within a reasonable amount of time, the evolution of computation is expected to reach the exascale level. One of several key challenges to overcome in these exascale systems is to handle the high rate of component failure arising due to having millions of cores working together with high power consumption and clock frequencies. Studies show that even the highly tuned widely used checkpointing technique is unable to handle the failures efficiently in exascale systems. The Sparse Grid Combination Technique (SGCT) is proved to be a cost-effective method for computing high-dimensional PDE based simulations with only small loss of accuracy, which can be easily modified to provide an Algorithm-Based Fault Tolerance (ABFT) for these applications. Additionally, the recently introduced User Level Failure Mitigation (ULFM) MPI library provides the ability to detect and identify application process failures, and reconstruct the failed processes. However, there is a gap of the research how these could be integrated together to develop fault-tolerant applications, and the range of issues that may arise in the process are yet to be revealed. My thesis is that with suitable infrastructural support an integration of ULFM MPI and a modified form of the SGCT can be used to create high performance robust PDE based applications. The key contributions of my thesis are: (1) An evaluation of the effectiveness of applying the modified version of the SGCT on three existing and complex applications (including a general advection solver) to make them highly fault-tolerant. (2) An evaluation of the capabilities of ULFM MPI to recover from a single or multiple real process/node failures for a range of complex applications computed with the modified form of the SGCT. (3) A detailed experimental evaluation of the fault-tolerant work including the time and space requirements, and parallelization on the non-SGCT dimensions. (4) An analysis of the result errors with respect to the number of failures. (5) An analysis of the ABFT and recovery overheads. (6) An in-depth comparison of the fault-tolerant SGCT based ABFT with traditional checkpointing on a non-fault-tolerant SGCT based application. (7) A detailed evaluation of the infrastructural support in terms of load balancing, pure- and hybrid-MPI, process layouts, processor affinity, and so on.

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Buchteile zum Thema "FAULTY NODE DETECTION"

Puthussery, Antony, und G. Muneeswari. „Faulty Node Detection Using Vertex Magic Total Labelling in Distributed System“. In Sustainable Communication Networks and Application, 619–30. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8677-4_50.

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Anand, Santosh, und B. P. Adithi. „Detection and Prevention of Faulty Node in Heterogeneous Wireless Sensor Network“. In Advances in Intelligent Systems and Computing, 383–97. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5301-8_29.

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Satish, E. G., und A. C. Ramachandra. „Faulty Node Detection and Correction of Route in Network-On-Chip (NoC)“. In Innovative Data Communication Technologies and Application, 783–89. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7167-8_57.

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Deshmukh, Ketki, und Avinash More. „Modified Long Short-Term Memory Algorithm for Faulty Node Detection Using node’s Raw Data Pattern“. In Data Management, Analytics and Innovation, 345–55. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1414-2_26.

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Mohapatra, Nibedita Priyadarsini, und Manjushree Nayak. „An Energy-Saving Approach for Routing in Wireless Sensor Networks with ML-Based Faulty Node Detection“. In Advances in IoT and Security with Computational Intelligence, 309–22. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5085-0_30.

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Nath, Nithish N., V. Radhamani Pillay und G. Saisuriyaa. „Distributed Node Fault Detection and Tolerance Algorithm for Controller Area Networks“. In Advances in Intelligent Systems and Computing, 247–57. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23258-4_22.

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Mondal, Bappaditya, Anirban Bhattacharjee, Subham Saha, Shalini Parekh, Chandan Bandyopadhyay und Hafizur Rahaman. „An Approach for Detection of Node Displacement Fault (NDF) in Reversible Circuit“. In Communications in Computer and Information Science, 605–16. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9767-8_50.

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Yang, Ming, Jun Huang und Fei Sun. „A Note on Actuator Fault Detection for One-Sided Lipschitz Systems“. In Lecture Notes in Electrical Engineering, 574–81. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9682-4_60.

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Banerjee, Indrajit, Prasenjit Chanak, Biplab Kumar Sikdar und Hafizur Rahaman. „DFDNM: A Distributed Fault Detection and Node Management Scheme for Wireless Sensor Network“. In Advances in Computing and Communications, 68–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22720-2_7.

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Das, Sukanta, Nazma N. Naskar, Sukanya Mukherjee, Mamata Dalui und Biplab K. Sikdar. „Characterization of CA Rules for SACA Targeting Detection of Faulty Nodes in WSN“. In Lecture Notes in Computer Science, 300–311. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15979-4_32.

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Konferenzberichte zum Thema "FAULTY NODE DETECTION"

Li, Wenjie, Laura Galluccio, Michel Kieffer und Francesca Bassi. „Distributed Faulty Node Detection in DTNs“. In 2016 25th International Conference on Computer Communication and Networks (ICCCN). IEEE, 2016. http://dx.doi.org/10.1109/icccn.2016.7568511.

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Royyan, Muhammad, Joong-Hyuk Cha, Jae-Min Lee und Dong-Seong Kim. „Data-driven faulty node detection scheme for Wireless Sensor Networks“. In 2017 Wireless Days (WD). IEEE, 2017. http://dx.doi.org/10.1109/wd.2017.7918145.

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Jadav, Pooja, und Vinoth K. Babu. „Fuzzy logic based faulty node detection in Wireless Sensor Network“. In 2017 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2017. http://dx.doi.org/10.1109/iccsp.2017.8286384.

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Li, Wenjie, Francesca Bassi, Michel Kieffer, Alex Calisti, Gianni Pasolini und Davide Dardari. „Distributed faulty node detection in DTNs in presence of Byzantine attack“. In ICC 2017 - 2017 IEEE International Conference on Communications. IEEE, 2017. http://dx.doi.org/10.1109/icc.2017.7996846.

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Dusane, Atul V., und Krishnakant P. Adhiya. „Detection of Faulty node with Hybrid Machine Learning using SVM model“. In 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES). IEEE, 2023. http://dx.doi.org/10.1109/cises58720.2023.10183576.

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Shial, Rabindra Kumar, Bhabani Sankar Gouda, Sudhir Ranjan Pattanaik und Nilambar Sethi. „A Centralized Faulty Node Detection Algorithm Based on Statistical Analysis in WSN“. In 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA). IEEE, 2020. http://dx.doi.org/10.1109/iccsea49143.2020.9132847.

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Ejbali, Ridha, Mourad Zaied, Jamila Atiga und Nour Elhouda Mbarki. „Faulty node detection in wireless sensor networks using a recurrent neural network“. In Tenth International Conference on Machine Vision (ICMV 2017), herausgegeben von Jianhong Zhou, Petia Radeva, Dmitry Nikolaev und Antanas Verikas. SPIE, 2018. http://dx.doi.org/10.1117/12.2314837.

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Zhu, Bing, Wenzhu Zhang, Wei Feng und Lin Zhang. „Distributed faulty node detection and isolation in delay-tolerant vehicular sensor networks“. In 2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC 2012). IEEE, 2012. http://dx.doi.org/10.1109/pimrc.2012.6362584.

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Phatak, Tejashree, und S. D. Sawarkar. „Enhancing QoS of Wireless Sensor Network by detection of faulty sensor node“. In 2016 International Conference on Computing, Analytics and Security Trends (CAST). IEEE, 2016. http://dx.doi.org/10.1109/cast.2016.7914948.

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Lalem, Farid, Ahcène Bounceur, Reinhardt Euler, Mohammad Hammoudeh, Rahim Kacimi und Sanaa Kawther Ghalem. „Distributed faulty sensor node detection in wireless sensor networks based on copula theory“. In ICC '17: Second International Conference on Internet of Things, Data and Cloud Computing. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3018896.3065837.

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