Fault Diagnosis in a Steam Generator Applying Fuzzy Clustering Techniques
暂无分享,去创建一个
Adrián Rodríguez Ramos | José Luis Verdegay Galdeano | Orestes Llanes Santiago | Rayner Domínguez García
[1] R. Storn,et al. Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .
[2] Inseok Hwang,et al. A Survey of Fault Detection, Isolation, and Reconfiguration Methods , 2010, IEEE Transactions on Control Systems Technology.
[3] V. Steffen,et al. Solution of inverse radiative transfer problems in two-layer participating media with differential evolution , 2010 .
[4] Jian-Da Wu,et al. Fault gear identification using vibration signal with discrete wavelet transform technique and fuzzy-logic inference , 2009, Expert Syst. Appl..
[5] Yan Yan Pang,et al. Fault Diagnosis Method Based on KPCA and Selective Neural Network Ensemble , 2014 .
[6] L. Felipe Blázquez,et al. Fuzzy logic-based decision-making for fault diagnosis in a DC motor , 2005, Eng. Appl. Artif. Intell..
[7] Rolf Isermann,et al. Fault-Diagnosis Applications: Model-Based Condition Monitoring: Actuators, Drives, Machinery, Plants, Sensors, and Fault-tolerant Systems , 2011 .
[8] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .
[9] Rainer Storn,et al. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..
[10] Jyoti Kiran,et al. Data Clustering Approach to Industrial Process Monitoring, Fault Detection and Isolation , 2011 .
[11] R. Storn,et al. Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .
[12] Diagnóstico de fallos en el generador de vapor BKZ-340-140-29M , 2011 .
[13] Qin Ming Liu,et al. The Study of Fault Diagnosis Based on Particle Swarm Optimization Algorithm , 2009, Comput. Inf. Sci..
[14] Si-Zhao Joe Qin,et al. Survey on data-driven industrial process monitoring and diagnosis , 2012, Annu. Rev. Control..
[15] Steven X. Ding,et al. Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools , 2008 .
[16] Cesar A. Uribe,et al. Unsupervised Feature Selection Based on Fuzzy Clustering for Fault Detection of the Tennessee Eastman Process , 2012, IBERAMIA.
[17] Ali Azadeh,et al. A fuzzy inference system for pump failure diagnosis to improve maintenance process: The case of a petrochemical industry , 2010, Expert Syst. Appl..
[18] Barry Lennox,et al. Monitoring a complex refining process using multivariate statistics , 2008 .
[19] Andries Petrus Engelbrecht,et al. An overview of clustering methods , 2007, Intell. Data Anal..
[20] Ujjwal Maulik,et al. Validity index for crisp and fuzzy clusters , 2004, Pattern Recognit..
[21] Claudia Isaza,et al. Unsupervised feature selection based on fuzzy partition optimization for industrial processes monitoring , 2011, 2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings.
[22] Khashayar Khorasani,et al. Dynamic neural network-based fault diagnosis of gas turbine engines , 2014, Neurocomputing.
[23] Jiangping Wang,et al. Vibration-based fault diagnosis of pump using fuzzy technique , 2006 .
[24] Joseph Aguilar-Martin,et al. Automaton based on fuzzy clustering methods for monitoring industrial processes , 2013, Eng. Appl. Artif. Intell..
[25] Dillwyn Williams,et al. The Chernobyl Accident 20 Years On: An Assessment of the Health Consequences and the International Response , 2006, Environmental health perspectives.
[26] Michael J. Brennan,et al. Structural damage detection by fuzzy clustering , 2008 .
[27] Raghunathan Rengaswamy,et al. A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..
[28] C. D. Gelatt,et al. Optimization by Simulated Annealing , 1983, Science.
[29] Raghunathan Rengaswamy,et al. A Novel Interval-Halving Framework For Automated Identification of Process Trends , 2004 .
[30] Raghunathan Rengaswamy,et al. A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies , 2003, Comput. Chem. Eng..
[31] Orestes Llanes-Santiago,et al. An approach for Fault Diagnosis based on bio-inspired strategies , 2010, IEEE Congress on Evolutionary Computation.
[32] Feng Zhao,et al. Learning kernel parameters for kernel Fisher discriminant analysis , 2013, Pattern Recognit. Lett..
[33] Silvio Simani,et al. Model-based fault diagnosis in dynamic systems using identification techniques , 2003 .
[34] Krzysztof Patan,et al. Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes , 2008 .
[35] Geok Soon Hong,et al. Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results , 2009 .
[36] Roli Varma,et al. The Bhopal Disaster of 1984 , 2005 .
[37] Marcin Witczak. Modelling and Estimation Strategies for Fault Diagnosis of Non-Linear Systems: From Analytical to Soft Computing Approaches , 2007 .
[38] Jicong Fan,et al. Fault detection and diagnosis of non-linear non-Gaussian dynamic processes using kernel dynamic independent component analysis , 2014, Inf. Sci..
[39] Paul Weston,et al. Fault detection and diagnosis for railway track circuits using neuro-fuzzy systems , 2008 .
[40] Rolf Isermann. Examples of fault-tolerant systems , 2011 .
[41] Oscar Camacho,et al. Fault diagnosis based on multivariate statistical techniques , 2007 .
[42] Miin-Shen Yang,et al. A cluster validity index for fuzzy clustering , 2005, Pattern Recognit. Lett..
[43] Nikolaos V. Sahinidis,et al. A Finite Algorithm for Global Minimization of Separable Concave Programs , 1998, J. Glob. Optim..
[44] Jian Xiao,et al. A novel chaotic particle swarm optimization based fuzzy clustering algorithm , 2012, Neurocomputing.
[45] Joseph Mathew,et al. Rotating machinery prognostics. State of the art, challenges and opportunities , 2009 .