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Relevant bibliographies by topics / Fault severity estimation

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Journal articles
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Academic literature on the topic 'Fault severity estimation'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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Journal articles on the topic "Fault severity estimation"

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Mharakurwa, Edwell T., G. N. Nyakoe, and A. O. Akumu. "Power Transformer Fault Severity Estimation Based on Dissolved Gas Analysis and Energy of Fault Formation Technique." Journal of Electrical and Computer Engineering 2019 (February 3, 2019): 1–10. http://dx.doi.org/10.1155/2019/9674054.

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Decision making on transformer insulation condition based on the evaluated incipient faults and aging stresses has been the norm for many asset managers. Despite being the extensively applied methodology in power transformer incipient fault detection, solely dissolved gas analysis (DGA) techniques cannot quantify the detected fault severity. Fault severity is the core property in transformer maintenance rankings. This paper presents a fuzzy logic methodology in determining transformer faults and severity through use of energy of fault formation of the evolved gasses during transformer faulting

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Kim, Heonkook, Hojin Lee, Seongyun Kim, and Sang Woo Kim. "Attention Recurrent Neural Network-Based Severity Estimation Method for Early-Stage Fault Diagnosis in Robot Harness Cable." Sensors 23, no. 11 (2023): 5299. http://dx.doi.org/10.3390/s23115299.

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Cable is crucial to the control and instrumentation of machines and facilities. Therefore, early diagnosis of cable faults is the most effective approach to prevent system downtime and maximize productivity. We focused on a “soft fault state”, which is a transient state that eventually becomes a permanent fault —open-circuit and short-circuit. However, the issue of soft fault diagnosis has not been considered enough in previous research, which could not provide crucial information, such as fault severity, to support maintenance. In this study, we focused on solving soft fault problem by estima

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Yang, Qingcai, Shuying Li, and Yunpeng Cao. "An IMM-GLR Approach for Marine Gas Turbine Gas Path Fault Diagnosis." Mathematical Problems in Engineering 2018 (September 18, 2018): 1–12. http://dx.doi.org/10.1155/2018/1918350.

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An IMM-GLR approach based on interacting multiple model (IMM) and generalized likelihood ratio (GLR) estimation was developed to detect, isolate, and estimate gas turbine gas path fault (including abrupt fault and multiple faults) in the underdetermine estimation conditions. In this approach, a model set representing gas turbine health condition and different fault condition was established, and a corresponding bank of filters was designed. An IMM-based FDI algorithm based on these filters is applied to detect and isolate fault, and a GLR estimation algorithm is used to estimate the fault seve

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Jakob, Fredi, and James J. Dukarm. "Thermodynamic Estimation of Transformer Fault Severity." IEEE Transactions on Power Delivery 30, no. 4 (2015): 1941–48. http://dx.doi.org/10.1109/tpwrd.2015.2415767.

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Lee, Hojin, Hyeyun Jeong, Seongyun Kim, and Sang Woo Kim. "Severity Estimation for Interturn Short-Circuit and Demagnetization Faults through Self-Attention Network." Sensors 22, no. 12 (2022): 4639. http://dx.doi.org/10.3390/s22124639.

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This study presents a novel interturn short-circuit fault (ISCF) and demagnetization fault (DF) diagnosis strategy based on a self-attention-based severity estimation network (SASEN). We analyze the effects of the ISCF and DF in a permanent-magnet synchronous machine and select appropriate inputs for estimating the fault severities, i.e., a positive-sequence voltage and current and negative-sequence voltage and current. The chosen inputs are fed into the SASEN to estimate fault indicators for quantifying the fault severities of the ISCF and DF. The SASEN comprises an encoder and decoder based

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Cen, Zhaohui, Hassan Noura, and Younes Al Younes. "Systematic Fault Tolerant Control Based on Adaptive Thau Observer Estimation for Quadrotor Uavs." International Journal of Applied Mathematics and Computer Science 25, no. 1 (2015): 159–74. http://dx.doi.org/10.1515/amcs-2015-0012.

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Abstract A systematic fault tolerant control (FTC) scheme based on fault estimation for a quadrotor actuator, which integrates normal control, active and passive FTC and fault parking is proposed in this paper. Firstly, an adaptive Thau observer (ATO) is presented to estimate the quadrotor rotor fault magnitudes, and then faults with different magnitudes and time-varying natures are rated into corresponding fault severity levels based on the pre-defined fault-tolerant boundaries. Secondly, a systematic FTC strategy which can coordinate various FTC methods is designed to compensate for failures

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Bouakoura, Mohamed, Mohamed-Said Naït-Saïd, and Nasreddine Nait-Said. "Incipient Inter-Turn Short Circuit Fault Estimation Based on a Faulty Model Observer and ANN-Method for Induction Motor Drives." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 12, no. 4 (2019): 374–83. http://dx.doi.org/10.2174/2352096511666180705113021.

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Background: According to statistics, short circuit faults are the second most frequent faults in induction motors. Thus, in this paper, we investigated inter turn short circuit faults in their early stage. Methods: A new equivalent model of the induction motor with turn to turn fault on one phase has been developed. This model has been used to establish two schemes to estimate the severity of the short circuit fault. In the first scheme, the faulty model is considered as an observer, where a correction of an error between the measured and the estimated currents is the kernel of the fault sever

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Fontes Godoy, Wagner, Daniel Morinigo-Sotelo, Oscar Duque-Perez, Ivan Nunes da Silva, Alessandro Goedtel, and Rodrigo Henrique Cunha Palácios. "Estimation of Bearing Fault Severity in Line-Connected and Inverter-Fed Three-Phase Induction Motors." Energies 13, no. 13 (2020): 3481. http://dx.doi.org/10.3390/en13133481.

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This paper addresses a comprehensive evaluation of a bearing fault evolution and its consequent prediction concerning the remaining useful life. The proper prediction of bearing faults in their early stage is a crucial factor for predictive maintenance and mainly for the production management schedule. The detection and estimation of the progressive evolution of a bearing fault are performed by monitoring the amplitude of the current signals at the time domain. Data gathered from line-fed and inverter-fed three-phase induction motors were used to validate the proposed approach. To assess class

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Wang, Ting, Liliuyuan Liang, Xinrang Feng, Ferdinanda Ponci, and Antonello Monti. "Online Parameter Estimation for Fault Identification in Multi-Terminal DC Distribution Grids." Energies 14, no. 18 (2021): 5630. http://dx.doi.org/10.3390/en14185630.

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Fast and accurate identification of short-circuit faults is important for post-fault service restoration and maintenance in DC distribution grids. Yet multiple power sources and complex system topologies complicate the fault identification in multi-terminal DC distribution grids. To address this challenge, this paper introduces an approach that achieves fast online identification of both the location and the severity of faults in multi-terminal DC distribution grids. First, a generic model describing the dynamic response of DC lines to both pole-to-ground and pole-to-pole faults with fault cur

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Nemati, Mohsen, and Saeed Seyedtabaii. "Fault severity estimation in a vehicle cooling system." Asian Journal of Control 22, no. 6 (2019): 2543–48. http://dx.doi.org/10.1002/asjc.2155.

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Dissertations / Theses on the topic "Fault severity estimation"

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Säfdal, Joakim. "Data-Driven Engine Fault Classification and Severity Estimation Using Interpolated Fault Modes from Limited Training Data." Thesis, Linköpings universitet, Fordonssystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-173916.

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Today modern vehicles are expected to be safe, environmentally friendly, durable and economical. Monitoring the health of the vehicle is therefore more important than ever. As the complexity of vehicular systems increases the need for efficient monitoring methods has increased as well. Traditional methods of deriving models for the systems are today not as efficient as the complexity of the systems increases the time and skill needed to implement the models. An alternative is data driven methods where a collection of data associated with the behavior of the system is used to draw conclusions o

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Lundgren, Andreas. "Data-Driven Engine Fault Classification and Severity Estimation Using Residuals and Data." Thesis, Linköpings universitet, Fordonssystem, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-165736.

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Recent technological advances in the automotive industry have made vehicularsystems increasingly complex in terms of both hardware and software. As thecomplexity of the systems increase, so does the complexity of efficient monitoringof these system. With increasing computational power the field of diagnosticsis becoming evermore focused on software solutions for detecting and classifyinganomalies in the supervised systems. Model-based methods utilize knowledgeabout the physical system to device nominal models of the system to detect deviations,while data-driven methods uses historical data to

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Yang, Junjie. "Fault Diagnosis and Prognosis in multivariate complex systems." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPAST001.

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Le diagnostic et le pronostic des défaillances ont suscité une attention considérable dans l'industrie et le monde universitaire en raison des exigences croissantes en matière de fiabilité, de disponibilité, de maintenabilité et de sécurité.Malgré les progrès significatifs, les méthodologies de diagnostic de défauts existantes souffrent toujours de problèmes, tels que le manque de données défectueuses suffisantes pour la formation, l'inefficacité des données distribuées complexes, la faible sensibilité aux défauts naissants et l'interférence du bruit et des valeurs aberrantes.Par conséquent, c

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Book chapters on the topic "Fault severity estimation"

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Moshou, Dimitrios, Dimitrios Kateris, Nader Sawalhi, Spyridon Loutridis, and Ioannis Gravalos. "Fault Severity Estimation in Rotating Mechanical Systems Using Feature Based Fusion and Self-Organizing Maps." In Artificial Neural Networks – ICANN 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15822-3_49.

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Gao, Cai-xia, Nan Zhang, and Hai-chao Feng. "Severity Estimation of Interturn Short Circuit Fault for Direct-Drive Permanent Magnet Synchronous Motor Based onKnowledge Graph System." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6320-8_55.

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dos Santos, Tiago, Fernando J. T. E. Ferreira, João Moura Pires, and Carlos Viegas Damásio. "Severity Estimation of Stator Winding Short-Circuit Faults Using Cubist." In Progress in Artificial Intelligence. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65340-2_18.

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Conference papers on the topic "Fault severity estimation"

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Dukarm, James, and Fredi Jakob. "Thermodynamic estimation of transformer fault severity." In 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D). IEEE, 2016. http://dx.doi.org/10.1109/tdc.2016.7519962.

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Noursadeghi, Elaheh, and Ioannis Raptis. "A Particle Filtering-Based Approach for Distributed Fault Diagnosis and Estimation of Multi-Robot Systems." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9789.

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This paper deals with the problem of designing a distributed fault diagnosis and estimation algorithm for multi-robot systems that are subject to faults in the form of abrupt velocity biases. To solve this problem, the multi-robot collective is converted to a network of interconnected diagnostic nodes (DNs) that is deployed to monitor the health of the system. Each node consists of a reduced-order estimator with relative state measurements and an online parameter learning filter. The local estimator executes a distributed variation of the particle filtering algorithm using the local sensor mea

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Yang, Junjie, and Claude Delpha. "Incipient Fault Severity Estimation Using Local Mahalanobis Distance." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9747618.

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Soualhi, Abdenour, and Sofiane Taleb. "Data fusion for fault severity estimation of ball bearings." In 2018 IEEE International Conference on Industrial Technology (ICIT). IEEE, 2018. http://dx.doi.org/10.1109/icit.2018.8352514.

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Corne, Bram, Jos Knockaert, and Jan Desmet. "Misalignment and unbalance fault severity estimation using stator current measurements." In 2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED). IEEE, 2017. http://dx.doi.org/10.1109/demped.2017.8062363.

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Nguyen, Viethung, Danwei Wang, Jeevanand Seshadrinath, Sivakumar Nadarajan, and Viswanathan Vaiyapuri. "Fault severity estimation using nonlinear Kalman filter for induction motors under inter-turn fault." In IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2016. http://dx.doi.org/10.1109/iecon.2016.7792962.

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Yaqub, M. F., I. Gondal, and J. Kamruzzaman. "Machine fault severity estimation based on adaptive wavelet nodes selection and SVM." In 2011 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2011. http://dx.doi.org/10.1109/icma.2011.5986279.

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Corne, Bram, Bram Vervisch, Stijn Derammelaere, Sergio M. A. Cruz, Jos Knockaert, and Jan Desmet. "Single point outer race bearing fault severity estimation using stator current measurements." In 2017 IEEE International Electric Machines and Drives Conference (IEMDC). IEEE, 2017. http://dx.doi.org/10.1109/iemdc.2017.8001869.

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Zhang, Lu, Claude Delpha, and Demba Diallo. "Fault Severity Estimation in 7-Phase Electrical Machines in a Noisy Environment." In 2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE). IEEE, 2023. http://dx.doi.org/10.1109/isie51358.2023.10227933.

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Zhang, Lu, Claude Delpha, and Demba Diallo. "Performance of Fault Severity Estimation in 7-Phase Electrical Machines under Noisy Conditions." In 2023 Prognostics and Health Management Conference (PHM). IEEE, 2023. http://dx.doi.org/10.1109/phm58589.2023.00053.

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