FSN-based fault modeling in CANDU® stations

Abstract Over the past several years a number of domestic CANDU® stations have experienced issues with neutron detection systems that challenged safety and operation. Intelligent troubleshooting methodology is required to aid in making risk-informed decisions related to design and operational activities, which can aid current stations and be used for the future generation of CANDU® designs. This paper presents fault modeling approach using Fault Semantic Network (FSN) with risk estimation. A case study of challenges with liquid zone level control at one of the domestic CANDU® stations is used to demonstrate this approach. This work will further continue into the development of fault forecasting using intelligent computational methods, such as Genetic Algorithms (GA), Fuzzy Logic, and Artificial Neural Network.

[1]  Hossam A. Gabbar.  Fault Semantic Networks for Accident Forecasting of LNG Plants , 2010, KES.

[2]  Keith E. Holbert,et al.  Nuclear Power Plant Instrumentation Fault Detection Using Fuzzy Logic , 2012 .

[3]  Hossam A. Gabbar,et al.  Plant object-orientated model formalization — case study: HDS plant design , 2003 .

[4]  Shen Yin,et al.  On PCA-based fault diagnosis techniques , 2010, 2010 Conference on Control and Fault-Tolerant Systems (SysTol).

[5]  Leo H. Chiang,et al.  Process monitoring using causal map and multivariate statistics: fault detection and identification , 2003 .

[6]  Poong Hyun Seong,et al.  Application of a fuzzy learning algorithm to nuclear steam generator level control , 1995 .

[7]  Lotfi A. Zadeh,et al.  Roles of Soft Computing and Fuzzy Logic in the Conception, Design and Deployment of Information/Intelligent Systems , 1998 .

[8]  Hans-Peter Kriegel,et al.  A General Framework for Increasing the Robustness of PCA-Based Correlation Clustering Algorithms , 2008, SSDBM.

[9]  Hossam A. Gabbar,et al.  Improved qualitative fault propagation analysis , 2007 .

[10]  Michio Sugeno,et al.  Industrial Applications of Fuzzy Control , 1985 .

[11]  Manuel Berenguel,et al.  Fuzzy logic control of a solar power plant , 1995, IEEE Trans. Fuzzy Syst..

[12]  Kazuhiko Suzuki.  Utilization of Unified Modeling Language (UML) to Represent the Artifacts of the Plant Design Model , 2000 .

[13]  Hossam A. Gabbar,et al.  Intelligent Topology Analyzer for Improved Plant Operation , 2006, 2006 SICE-ICASE International Joint Conference.

[14]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[15]  Jin Hyun Park,et al.  Fault detection and identification of nonlinear processes based on kernel PCA , 2005 .

[16]  P.M. Frank,et al.  A fault diagnosis concept for a high-pressure-preheater line , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[17]  Anand Sivasubramaniam,et al.  Critical event prediction for proactive management in large-scale computer clusters , 2003, KDD '03.

[18]  Royston Goodacre,et al.  Rapid Differentiation of Closely RelatedCandida Species and Strains by Pyrolysis-Mass Spectrometry and Fourier Transform-Infrared Spectroscopy , 1998, Journal of Clinical Microbiology.

[19]  Kee-Choon Kwon,et al.  A study on water level control of PWR steam generator at low power and the self-tuning of its fuzzy controller , 1995, Fuzzy Sets Syst..

[20]  Hossam A. Gabbar,et al.  Faults Forecasting System , 2009 .

[21]  Okyay Kaynak,et al.  Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications , 1998, NATO ASI Series.

[22]  Felipe Núñez,et al.  Fault tolerant measurement system based on Takagi-Sugeno fuzzy models for a gas turbine in a combined cycle power plant , 2011, Fuzzy Sets Syst..

[23]  Nikunj C. Oza,et al.  Online Ensemble Learning , 2000, AAAI/IAAI.

[24]  Juergen Hahn,et al.  Fault detection and classification in chemical processes based on neural networks with feature extraction. , 2003, ISA transactions.

[25]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[26]  Shuqing Wang,et al.  Fault diagnosis in industrial processes using principal component analysis and hidden Markov model , 2004, Proceedings of the 2004 American Control Conference.

[27]  Zhiling Lan,et al.  A practical failure prediction with location and lead time for Blue Gene/P , 2010, 2010 International Conference on Dependable Systems and Networks Workshops (DSN-W).

[28]  Wenli Yang,et al.  Fuzzy Fault Diagnosis and Accommodation System for Hybrid Fuel-Cell/Gas-Turbine Power Plant , 2010, IEEE Transactions on Energy Conversion.

[29]  Raghunathan Rengaswamy,et al.  A fast training neural network and its updation for incipient fault detection and diagnosis , 2000 .

[30]  Aldo Cipriano,et al.  Fuzzy predictive control of a solar power plant , 2005, IEEE Transactions on Fuzzy Systems.

[31]  Zhiling Lan,et al.  Dynamic Meta-Learning for Failure Prediction in Large-Scale Systems: A Case Study , 2008, 2008 37th International Conference on Parallel Processing.

[32]  Indra Narayan Kar,et al.  Estimating Compressor Discharge Pressure of Gas Turbine Power Plant Using Type-2 Fuzzy Logic Systems , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[33]  C Rosen,et al.  Multivariate and multiscale monitoring of wastewater treatment operation. , 2001, Water research.

[34]  A. Stewart Fotheringham,et al.  Principal Component Analysis on Spatial Data: An Overview , 2013 .

[35]  S.M. Virk,et al.  Fault Prediction Using Artificial Neural Network and Fuzzy Logic , 2008, 2008 Seventh Mexican International Conference on Artificial Intelligence.

[36]  Michael Lawrence,et al.  The effects of structural characteristics of explanations on use of a DSS , 2006, Decis. Support Syst..

[37]  Chris Westbury,et al.  Using genetic programming to discover nonlinear variable interactions , 2003, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[38]  D B Kell,et al.  Rapid screening for metabolite overproduction in fermentor broths, using pyrolysis mass spectrometry with multivariate calibration and artificial neural networks , 1994, Biotechnology and bioengineering.

[39]  P. Sanguansat.  Principal Component Analysis , 2012 .

[40]  N. Pettorelli,et al.  Coupling Principal Component Analysis and GIS to map deer habitats , 2005 .