A hybrid fault diagnosis methodology with support vector machine and improved particle swarm optimization for nuclear power plants
Tóm tắt
Từ khóa
Tài liệu tham khảo
Idaho National Laboratory. Report from the light water reactor sustainability. In: Workshop on on-line monitoring technologies. INL/EXT-10-19500. 2010.
International Atomic Energy Agency, 2013
International Atomic Energy Agency, 2008
Peng, 2016, Methodology for analyzing the dependencies between human operators in digital control system, Fuzzy Sets and Systems, 293, 127, 10.1016/j.fss.2015.04.002
Song, 2013, An analysis of technical security control requirements for digital I & C systems in nuclear power plant, Nucl Eng Technol, 45, 637, 10.5516/NET.04.2012.091
Chung, 1994, Incipient multiple fault diagnosis in real time with application to large-scale system, IEEE Trans Nucl Sci, 41, 1692, 10.1109/23.322777
Qin SJ. Survey on data-driven industrial process monitoring and diagnosis. Annu Rev Control 36(2):220-234.
Hadad, 2011, Fault diagnosis and classification based on wavelet transform and neural network, Prog Nucl Energy, 53, 41, 10.1016/j.pnucene.2010.09.006
Wolbrecht, 2000, Monitoring and diagnosis of a multi-stage manufacturing process using Bayesian networks, Artif Intell Eng Des Manuf, 14, 53
Zhang, 2019, Fault detection and diagnosis based on particle filters combined with interactive multiple-model estimation in dynamic process systems, ISA Trans., 85, 247, 10.1016/j.isatra.2018.10.015
Lind, 2011, An introduction to multilevel flow modeling, J Nucl Saf Simul, 2, 22
Kramer, 1987, A rule-based approach to fault diagnosis using the signed directed graph, AIChE J, 33, 1067, 10.1002/aic.690330703
Wang, 2016, Improved methods of online monitoring and prediction in condensate and feed water system of nuclear power plant, Ann. Nucl. Energy, 90, 44, 10.1016/j.anucene.2015.11.037
Thomas, 2015
Chu YY, Yang M, Yang F. Design of an operator support system for online maintenance at nuclear power plant. In: Proceedings of International Symposium on Future I & C for Nuclear Power Plants, ICI2011. Daejeon, Korea. August 21–25, 2011.
Peng, 2018, An intelligent hybrid methodology of on-line system-level fault diagnosis for nuclear power plant, Nucl Eng Technol, 50, 396, 10.1016/j.net.2017.11.014
Wang, 2017, An integrated data-driven methodology for early fault detection and diagnosis in nuclear power plant, Int J Nucl Saf Simul, 8, 225
Kramer, 1987, A rule-based approach to fault diagnosis using the signed directed graph, AIChE J, 33, 1067, 10.1002/aic.690330703
Ramesha, 1992, Knowledge-based diagnostic systems for continuous process operations based upon the task framework, Comput Chem Eng, 16, 109, 10.1016/0098-1354(92)80009-X
Wang, 2009, Data driven fault diagnosis and fault tolerant control: some advances and possible new directions, Acta Automat Sinica, 35, 739
Ranaee, 2010, Application of the PSO–SVM model for recognition of control chart patterns, ISA Trans., 49, 577, 10.1016/j.isatra.2010.06.005
Kwon, 2002, Hidden Markov models-based real-time transient identifications in nuclear power plants, Int. J. Intell. Syst., 17, 791, 10.1002/int.10050
Park, 2016, Transient diagnosis and prognosis for secondary system in nuclear power plants, Nucl Eng Technol, 48, 1184, 10.1016/j.net.2016.03.009
Ge, 2017, Review on data-driven modeling and monitoring for plant-wide industrial processes, Chemometr Intell Lab Syst, 171, 16, 10.1016/j.chemolab.2017.09.021
Hsu, 2002, A comparison of methods for multi-class support vector machines, IEEE Trans Neural Netw, 13, 415, 10.1109/72.991427
Wu, 2019, An improved particle swarm optimization algorithm for reliability-redundancy allocation problem with mixed redundancy strategy and heterogeneous components, Reliab Eng Syst Saf, 181, 62, 10.1016/j.ress.2018.09.005
Vapnik, 1981, The necessary and sufficient conditions for the uniform convergence of averages to their expected values, Teor Veroyatn Primen, 26
Vapnik, 1998, 175
Kennedy J, Eberhart R. Particle swarm optimization. In: Proceeding of IEEE international conference on neural networks. Perth: 1995. p. 1942–48.
Messaoud, 2019, Observer for nonlinear systems using mean value theorem and particle swarm optimization algorithm, ISA Trans., 85, 226, 10.1016/j.isatra.2018.10.036
Riccardo, 2007, Particle swarm optimization-an overview, Swarm Intell, 1, 33, 10.1007/s11721-007-0002-0
Shi, 1998, A modified particle swarm optimizer, 69
Mohammadia, 2018, Intelligent parameter optimization of savonius rotor using artificial neural network and genetic algorithm, Energy, 143, 56, 10.1016/j.energy.2017.10.121
Angeline, 1998, Using selection to improve particle swarm optimization, 84
Ji Z, Liao HL, Wang YW et al. A novel intelligent particle optimizer for global optimization of multimodal functions. In: IEEE congress on evolutionary computation. Singapore: 2007.
Peng, 2017, Real-time simulations to enhance distributed on-line monitoring and fault detection in pressurized water reactors, Ann. Nucl. Energy, 85, 259
Liu, 2017, Hybrid intelligent multi-fault detection method for rotating machinery based on RSGWPT, KPCA and twin SVM, ISA Trans., 66, 249, 10.1016/j.isatra.2016.11.001
He, 2015, A novel artificial fish swarm algorithm for solving large-scale reliability–redundancy application problem, ISA Trans., 59, 105, 10.1016/j.isatra.2015.09.015
Atashi, 2017, Breast cancer risk assessment using adaptive neuro-fuzzy inference system (ANFIS) and subtractive clustering algorithm, Multidiscip Cancer Investig, 1, 20, 10.21859/mci-01029