NERI PROJECT 99-119. TASK 2. DATA-DRIVEN PREDICTION OF PROCESS VARIABLES. FINAL REPORT

This report describes the detailed results for task 2 of DOE-NERI project number 99-119 entitled ''Automatic Development of Highly Reliable Control Architecture for Future Nuclear Power Plants''. This project is a collaboration effort between the Oak Ridge National Laboratory (ORNL,) The University of Tennessee, Knoxville (UTK) and the North Carolina State University (NCSU). UTK is the lead organization for Task 2 under contract number DE-FG03-99SF21906. Under task 2 we completed the development of data-driven models for the characterization of sub-system dynamics for predicting state variables, control functions, and expected control actions. We have also developed the ''Principal Component Analysis (PCA)'' approach for mapping system measurements, and a nonlinear system modeling approach called the ''Group Method of Data Handling (GMDH)'' with rational functions, and includes temporal data information for transient characterization. The majority of the results are presented in detailed reports for Phases 1 through 3 of our research, which are attached to this report.

[1]  Kjell Arne Barmsnes,et al.  PICASSO: A User Interface Management System for Real-Time Applications , 1992 .

[2]  Belle R. Upadhyaya,et al.  Multivariate statistical signal processing technique for fault detection and diagnostics , 1990 .

[3]  Terje Johnsen,et al.  Implementation of Graphical User Interfaces in Nuclear Applications , 1997 .

[4]  Stanley J. Farlow,et al.  Self-Organizing Methods in Modeling: Gmdh Type Algorithms , 1984 .

[5]  A. Erbay,et al.  A Personal Computer-Based On-Line Signal Validation System for Nuclear Power Plants , 1997 .

[6]  Masoud Naghedolfeizi,et al.  Dynamic Modeling of a Pressurized Water Reactor Plant for Diagnostics and Control , 1990 .

[7]  David Singer,et al.  Augmented Models for Statistical Fault Isolation in Complex Dynamic Systems , 1985, 1985 American Control Conference.

[8]  Don W. Miller,et al.  FAULT DETECTION AND ISOLATION : A HYBRID APPROACH , 2000 .

[9]  Belle R. Upadhyaya,et al.  Incipient Fault Detection and Isolation of Field Devices in Nuclear Power Systems Using Principal Component Analysis , 2001 .

[10]  Harold Lee Jones,et al.  Failure detection in linear systems , 1973 .

[11]  I. Jolliffe.  Principal Component Analysis , 2002 .

[12]  Silvio Simani,et al.  Model-based fault diagnosis in dynamic systems using identification techniques , 2003 .

[13]  J. W. Hines,et al.  A hybrid approach for detecting and isolating faults in nuclear power plant interacting systems , 1996 .

[14]  Richard D. Braatz,et al.  Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes , 2000 .

[15]  S. Wold.  Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models , 1978 .

[16]  Dean G. Blevins.  Introduction 9-2 , 1969 .

[17]  Barry M. Wise,et al.  The process chemometrics approach to process monitoring and fault detection , 1995 .

[18]  Paulo Brasko Ferreira.  Incipient fault detection and isolation of sensors and field devices , 1999 .

[19]  Pau-Lo Hsu,et al.  Robust Fault Detection and Isolation with Unstructured Uncertainty Using Eigenstructure Assignment , 1998 .

[20]  Seongkyu Yoon,et al.  Fault diagnosis with multivariate statistical models part I: using steady state fault signatures , 2001 .

[21]  David G. Stork,et al.  Pattern Classification , 1973 .

[22]  A. J. Morris,et al.  An overview of multivariate statistical process control in continuous and batch process performance monitoring , 1996 .

[23]  L. Biegler,et al.  Data reconciliation and gross‐error detection for dynamic systems , 1996 .

[24]  Luis J. de Miguel,et al.  Fault-diagnostic system using analytical fuzzy redundancy , 2000 .

[25]  Chi Hau Chen,et al.  Statistical Pattern Recognition. , 1973 .

[26]  C. M. Crowe,et al.  Data reconciliation — Progress and challenges , 1996 .

[27]  J. E. Jackson,et al.  Control Procedures for Residuals Associated With Principal Component Analysis , 1979 .

[28]  Sylviane Gentil,et al.  Model-based causal reasoning for process supervision , 1994, Autom..

[29]  Robert E. Uhrig,et al.  Signal Validation Using an Adaptive Neural Fuzzy Inference System , 1997 .

[30]  Jonathan Amsterdam,et al.  Automated qualitative modeling of dynamic physical systems , 1993 .

[31]  M. Hou,et al.  Multivariate statistical analysis of mineral processing plant data , 1993 .

[32]  Tony Springall.  Common Principal Components and Related Multivariate Models , 1991 .

[33]  Rami Mangoubi,et al.  Robust Estimation and Failure Detection , 1998 .

[34]  Venkat Venkatasubramanian,et al.  PCA-SDG based process monitoring and fault diagnosis , 1999 .

[35]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[36]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results , 1990, Autom..

[37]  Peter F. Patel-Schneider,et al.  DLP System Description , 1998, Description Logics.

[38]  Weihua Li,et al.  Detection, identification, and reconstruction of faulty sensors with maximized sensitivity , 1999 .

[39]  V. J. Vandoren.  Advanced control software goes beyond PID , 1998 .

[40]  B. R. Upadhyaya,et al.  Fault Detection and Isolation of Nuclear Plant System Sensors and Field Devices , 2002 .

[41]  Andrew R. Webb,et al.  Statistical Pattern Recognition , 1999 .

[42]  B. R. Upadhyaya,et al.  Hybrid digital signal processing and neural networks for automated diagnostics using NDE methods , 1993 .

[43]  Robert E. Uhrig,et al.  INFERENTIAL NEURAL NETWORKS FOR NUCLEAR POWER PLANT SENSOR CHANNEL DRIFT MONITORING , 1996 .

[44]  Ali Seyfettin Erbay.  A PC-Based Signal Validation System for Nuclear Power Plants , 1994 .

[45]  Duc Truong Pham,et al.  Neural Networks for Identification, Prediction and Control , 1995 .

[46]  Ten-Huei Guo,et al.  Integrated Health Monitoring and Controls for Rocket Engines , 1992 .

[47]  Asok Ray,et al.  A Redundancy Management Procedure for Fault Detection and Isolation , 1986 .

[48]  Ali Cinar,et al.  Statistical process monitoring and disturbance diagnosis in multivariable continuous processes , 1996 .

[49]  O. Glöckler,et al.  Application of reactor noise analysis in the candu reactors of Ontario hydro , 1995 .

[50]  T. McAvoy,et al.  Batch tracking via nonlinear principal component analysis , 1996 .

[51]  T.-H. Guo,et al.  Neural network based sensor validation for reusable rocket engines , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[52]  Sophocles J. Orfanidis,et al.  Introduction to signal processing , 1995 .

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

[54]  Satosi Watanabe,et al.  Methodologies of Pattern Recognition , 1969 .

[55]  Thomas F. Edgar,et al.  Sensor Fault Identification and Reconstruction Using Principal Component Analysis , 1996 .

[56]  B. Skagerberg,et al.  Multivariate data analysis applied to low-density polyethylene reactors , 1992 .

[57]  Yoon Joon Lee,et al.  The Level Control System Design of the Nuclear Steam Generator for Robustness and Performance , 2000 .

[58]  Hongwei Tong,et al.  Detection of gross erros in data reconciliation by principal component analysis , 1995 .

[59]  Sankar K. Pal,et al.  Genetic Algorithms for Pattern Recognition , 2017 .

[60]  M. Kramer.  Nonlinear principal component analysis using autoassociative neural networks , 1991 .

[61]  Jacky Montmain,et al.  Dynamic causal model diagnostic reasoning for online technical process supervision , 2000, Autom..

[62]  Seongkyu Yoon,et al.  Statistical and causal model‐based approaches to fault detection and isolation , 2000 .

[63]  Andrew Kusiak,et al.  Analysis of process models , 2000 .

[64]  Jie Chen,et al.  Robust Model-Based Fault Diagnosis for Dynamic Systems , 1998, The International Series on Asian Studies in Computer and Information Science.

[65]  Venkat Venkatasubramanian,et al.  Challenges in the industrial applications of fault diagnostic systems , 2000 .

[66]  J. Wesley Hines,et al.  Instrument Surveillance and Calibration Verification Through Plant Wide Monitoring Using Autoassociative Neural Networks , 1996 .

[67]  James F. Davis,et al.  Unbiased estimation of gross errors in process measurements , 1992 .

[68]  Randall Davis,et al.  Diagnosis Via Causal Reasoning: Paths of Interaction and the Locality Principle , 1989, AAAI.

[69]  David Mautner Himmelblau,et al.  Fault detection and diagnosis in chemical and petrochemical processes , 1978 .

[70]  Belle R. Upadhyaya,et al.  Monitoring feedwater flow rate and component thermal performance of pressurized water reactors by means of artificial neural networks , 1994 .

[71]  Robert E. Uhrig,et al.  Nonlinear Partial Least Squares Modeling for Instrument Surveillance and Calibration Verification , 2000 .

[72]  John T. Kay,et al.  Predictive adaptive control aids pulp digestion , 1997 .

[73]  Mark A. Kramer,et al.  A rule‐based approach to fault diagnosis using the signed directed graph , 1987 .

[74]  Panagiotis D. Christofides,et al.  Non-linear feedback control of parabolic partial differential difference equation systems , 2000 .

[75]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[76]  Barry M. Wise,et al.  Application of multi-way principal components analysis to nuclear waste storage tank monitoring , 1996 .

[77]  David Clarke,et al.  Local Sensor Validation , 1989 .

[78]  Chonghun Han,et al.  Multiple-Fault Diagnosis under Uncertain Conditions by the Quantification of Qualitative Relations , 1999 .

[79]  David M. Himmelblau,et al.  Dynamic rectification of data via recurrent neural nets and the extended Kalman filter , 1996 .

[80]  James B. Rawlings,et al.  Tutorial overview of model predictive control , 2000 .

[81]  Alberto Isidori,et al.  A Geometric Approach to Nonlinear Fault Detection and Isolation , 2000 .

[82]  Weihua Li,et al.  Isolation enhanced principal component analysis , 1999 .

[83]  T. McAvoy,et al.  Nonlinear principal component analysis—Based on principal curves and neural networks , 1996 .

[84]  Pieter J. Mosterman,et al.  Monitoring, Prediction, and Fault Isolation in Dynamic Physical Systems , 1997, AAAI/IAAI.

[85]  Robert E. Uhrig,et al.  ON-LINE SENSOR CALIBRATION MONITORING AND FAULT DETECTION FOR CHEMICAL PROCESSES , 1998 .

[86]  Terje Johnsen,et al.  DEVELOPING GRAPHICS APPLICATIONS IN AN INTERACTIVE ENVIRONMENT , 1994 .

[87]  Stephen Piche,et al.  Nonlinear model predictive control using neural networks , 2000 .

[88]  Liya Hou,et al.  Diagnosis of multiple simultaneous fault via hierarchical artificial neural networks , 1994 .

[89]  Xiao Xu,et al.  Sensor Validation and Fault Detection Using Neural Networks , 1999 .

[90]  Terje Johnsen,et al.  The Picasso-3 User Interface Management System , 1994 .

[91]  J. Fox.  Applied Regression Analysis, Linear Models, and Related Methods , 1997 .