Power Plant Surveillance and Diagnostics : Applied Research with Artificial Intelligence

This edited book is directed primarily to the discussion of the most recent developments and on-going research related to all areas pertaining to plant surveillance and diagnosis. The secondary aim of this book is to identify the successful applications of already well-settled methodological tools in the field. It will highlight advantages of intelligent systems, AI techniques, and integration of soft computing tools and traditional tools, for a better service in all aspects related to power plant surveillance and diagnostics. It also reports recent research results and provides a state of the art on AI in power plant surveillance and diagnostics. The book especially focuses on theoretical and analytical solutions to the problems of real interest in AI techniques, possibly combined with other traditional computing tools.

Foreword
Preface

Chapter 1 : Modern Approaches and Advanced Applications for Plant Surveillance and Diagnostics: An Overview
Chapter 2 : Regulatory Treatment of On-Line Surveillance and Diagnostic Systems
Chapter 3 : Optimized Maintenance and Management of Ageing of Critical Equipment in Nuclear Power Plants
Chapter 4 : Overview of Recent KFKI AEKI Activities in the Field of Plant Surveillance and Diagnostics
Chapter 5 : Adaptive Model-Based Control of Non-Linear Plants Using Soft Computing Techniques
Chapter 6 : Bayesian Networks in Decision Support
Chapter 7 : Hidden Markov Model Based Transient Identification in NPPs
Chapter 8 : Expert System-Based Implementation of Failure Detection
Chapter 9 : Detection of Incipient Signal or Process Faults in a Co-Generation Plant Using the Plant ECM System
Chapter 10 : On-Line Determination of the MTC (Moderator Temperature Coefficient) by Neutron Noise and Gamma-Thermometer Signals
Chapter 11 : Detecting Impacting of BWR Instrument Tubes by Wavelet Analysis
Chapter 12 : Development of Advanced Core Noise Monitoring System for a Boiling Water Reactor
Chapter 13 : Diagnosis of Measuring Systems Using Cluster Analysis Applied to Hydrostatic Water Level Measurement
Chapter 14 : A Hybrid Fuzzy-Fractal Approach for Time Series Analysis and Prediction and Its Applications to Plant Monitoring
Chapter 15 : Failure Detection Using a Fuzzy Neural Network With an Automatic Input Selection Algorithm
Chapter 16 : Artificial Neural Networks Modeling as a Diagnostic and Decision Making Tool
Chapter 17 : A New Approach for Transient Identification With “Don’t Know” Response Using Neural Networks
Chapter 18 : Planning Surveillance Test Policies Through Genetic Algorithms
Chapter 19 : A Possibilistic Approach for Transient Identification With “Don’t Know” Response Capability Optimized by Genetic Algorithm
Chapter 20 : Regularization of III-Posed Surveillance and Diagnostic Measurements
Chapter 21 : Application of Neuro-Fuzzy Logic for Early Detection and Diagnostics in Gas Plants and Combustion Chambers at ENEA
Chapter 22 : ALADDIN: Event Recognition & Fault Diagnosis for Process & Machine Condition Monitoring
Chapter 23 : PEANO and On-Line Monitoring Techniques for Calibration Reduction of Process Instrumentation in Power Plants

Index