Survey of Artificial Intelligence Methods for Detection and Identification of Component Faults in Nuclear Power Plants

A comprehensive survey of computer-based systems that apply artificial intelligence methods to detect and identify component faults in nuclear power plants is presented. Classification criteria are established that categorize artificial intelligence diagnostic systems according to the types of computing approaches used (e.g., computing tools, computer languages, and shell and simulation programs), the types of methodologies employed (e.g., types of knowledge, reasoning and inference mechanisms, and diagnostic approach), and the scope of the system. The major issues of process diagnostics and computer-based diagnostic systems are identified and cross-correlated with the various categories used for classification. Ninety-five publications are reviewed.

[1]  Robert P. Martin,et al.  CATALisp - an expert system for real-time diagnosis of multiple-failure transients in pressurized water reactors , 1988 .

[2]  J. Reifman,et al.  PRODIAG -- Dynamic qualitative analysis for process fault diagnosis , 1995 .

[3]  Jean-Michel Renders,et al.  A prototype neural network to perform early warning in nuclear power plant , 1995, Fuzzy Sets Syst..

[4]  Norio Naito,et al.  A real-time expert system for nuclear power plant failure diagnosis and operational guide , 1987 .

[5]  Poong Hyun Seong,et al.  Incipient multiple fault diagnosis in real time with application to large-scale systems , 1994 .

[6]  John A. Bernard,et al.  Expert Systems Applications Within the Nuclear Industry , 1989 .

[7]  Jie Lin,et al.  Predicting the severity of nuclear power plant transients using nearest neighbors modeling optimized by genetic algorithms on a parallel computer , 1995 .

[8]  William R. Nelson,et al.  REACTOR: An Expert System for Diagnosis and Treatment of Nuclear Reactor Accidents , 1982, AAAI.

[9]  Jie Lin,et al.  Nuclear power plant transient diagnostics using artificial neural networks that allow 'don't-know' classifications , 1995 .

[10]  J. Reifman,et al.  Statistical and optimization methods to expedite neural network training for transient identification , 1993 .

[11]  J. Reifman,et al.  Generating rules in expert systems for nuclear power plant diagnostics , 1987 .

[12]  Kazuo Yoshida,et al.  Development of reactor accident diagnostic system DISKET using knowledge engineering technique. , 1986 .

[13]  R. E. Uhrig,et al.  Using genetic algorithms to select inputs for neural networks , 1992, [Proceedings] COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks.

[14]  Javier E. Vitela,et al.  Accelerating learning of neural networks with conjugate gradients for nuclear power plant applications , 1994 .

[15]  Y. Inaba,et al.  Development of expert system for nuclear power plants feedwater system diagnosis , 1991 .

[16]  J. Reifman,et al.  Improving learning of neural networks for nuclear power plant transient classification , 1992 .

[17]  Lefteri H. Tsoukalas,et al.  Plant monitoring using possibilistic-probabilistic methodologies , 1987 .

[18]  S. M. Mirsky,et al.  Guidelines for the Verification and Validation of Expert System Software and Conventional Software , 1995 .

[19]  J. Reifman,et al.  Fuzzy diagnosis of multiple failures in nuclear power plants , 1991 .

[20]  Don W. Miller,et al.  A Real-Time Expert System for the Detection and Diagnosis of Abnormal Conditions in Nuclear Power Plants , 1991 .

[21]  Soon Heung Chang,et al.  Application of neural networks to a connectionist expert system for transient identification in nuclear power plants , 1993 .

[22]  C. E. Beck,et al.  A bibliography of technical papers relating expert systems to nuclear power plants , 1993 .

[23]  John C. Lee,et al.  A simulation-based expert system for nuclear power plant diagnostics , 1989 .

[24]  J. Reifman,et al.  Enhanced backpropagation training algorithm for transient event identification , 1993 .

[25]  Soon Heung Chang,et al.  The manipulation of time-varying dynamic variables using the rule modification method and performance-index in NPP accident diagnostic expert systems , 1987 .

[26]  E. H. Groundwater,et al.  Approaches to the verification and validation of expert systems for nuclear power plants: Final report , 1987 .

[27]  Takashi Washio,et al.  Automated derivation of failure symptoms for diagnosis of a nuclear power plant , 1986 .

[28]  A. Z. Akcasu,et al.  Probabilistic techniques using Monte Carlo sampling for multi- component system diagnostics , 1995 .

[29]  Alexander G. Parlos,et al.  Incipient fault detection and identification in process systems using accelerated neural network learning , 1994 .

[30]  John C. Lee,et al.  Macroscopic Mass and Energy Balance for Nuclear Plant Diagnostics Using Fuzzy Logic , 1988 .

[31]  Eric B. Bartlett,et al.  Detecting faults in a nuclear power plant by using dynamic node architecture artificial neural networks , 1994 .

[32]  R. E. Uhrig.  Potential application of neural networks to the operation of nuclear power plants , 1991 .

[33]  P. J. Rank,et al.  Fuzzy logic approach for transient diagnostics in nuclear power plants , 1994 .

[34]  Eric B. Bartlett,et al.  Error Prediction for a Nuclear Power Plant Fault-Diagnostic Advisor Using Neural Networks , 1994 .

[35]  H. Motoda,et al.  Knowledge-based approach to plant diagnosis , 1983 .

[36]  Eric B. Bartlett,et al.  Nuclear power plant status diagnostics using an artificial neural network , 1992 .

[37]  Alex Galperin,et al.  Use of qualitative and quantitative models in the supervision of power plant operations , 1991 .

[38]  J. Reifman,et al.  Cooperative research and development for artificial intelligence based reactor diagnostic system , 1994 .

[39]  Charles A. Pratt.  The society for computer simulation , 1984, SIML.

[40]  I. S. Kim,et al.  Implementation of MOAS II diagnosis system at the OECD Halden Reactor project , 1995 .

[41]  David Okrent,et al.  The use of neural networks and a prototype expert system in BWR ATWS accidents diagnosis , 1994 .

[42]  Soon-Heung Chang,et al.  Development of an expert system for failure diagnosis of primary side systems , 1992 .

[43]  Soon Heung Chang,et al.  Development of the on-line operator aid system OASYS using a rule-based expert system and fuzzy logic for nuclear power plants , 1995 .

[44]  O. Uluyol,et al.  Temporal neural network for the identification of nuclear power pant transients , 1993 .

[45]  Lefteri H. Tsoukalas,et al.  A hybrid neural network---fuzzy logic approach to nuclear power plant transient identification , 1991 .

[46]  C. Zheng,et al.  ; 0 ; , 1951 .

[47]  J. Reifman,et al.  Detection and diagnosis of abnormal transients in nuclear power plants , 1991 .

[48]  J. Reifman,et al.  Prodiag--a hybrid artificial intelligence based reactor diagnostic system for process faults , 1996 .

[49]  Lefteri H. Tsoukalas,et al.  Monitoring performance of devices using a coupled probability- possibility method , 1988 .

[50]  R. E. Uhrig,et al.  Accident scenario diagnostics with neural networks , 1992 .

[51]  Heikki Jokineva,et al.  Early fault detection and signal validation at the loviisa nuclear power plant , 1991 .

[52]  J. Reifman,et al.  Systematic generation of rules for nuclear power plant diagnostics , 1988 .

[53]  Enrico Zio,et al.  Fault Diagnosis Via Neural Networks: The Boltzmann Machine , 1994 .

[54]  Jaques Reifman,et al.  Systematic Construction of Qualitative Physics-Based Rules for Process Diagnostics , 1995, EPIA.

[55]  Kazuo Yoshida,et al.  An Approach to Build a Knowledge Base for Reactor Accident Diagnostic Expert System , 1988 .

[56]  Kee-Choon Kwon,et al.  Application of hidden Markov model to transient identification in nuclear power plants = 은닉 마르코프 모델을 이용한 원자력발전소 사고진단 , 1999 .

[57]  J. Reifman,et al.  Function-based approach to plant diagnosis , 1995 .

[58]  Jaques Reifman,et al.  Reactor diagnostics rule generation through statistical pattern recognition , 1991 .

[59]  Hiroshi Motoda,et al.  A Knowledge based System for Plant Diagnosis , 1984, Fifth Generation Computer Systems.

[60]  Don W. Miller,et al.  An integrated operator advisor system for plant monitoring, procedure management, and diagnosis , 1990 .

[61]  Hiroshi Seki,et al.  Abnormal Event Identification in Nuclear Power Plants Using a Neural Network and Knowledge Processing , 1993 .