Artificial Intelligence in Process Fault Diagnosis: Methods for Plant Surveillance

COVER
TITLE PAGE
COPYRIGHT PAGE
DEDICATION PAGE
CONTENTS
LIST OF CONTRIBUTORS
FOREWORD
PREFACE
ACKNOWLEDGMENTS
CHAPTER 1 MOTIVATIONS FOR AUTOMATING PROCESS FAULT ANALYSIS
	OVERVIEW
	CHAPTER HIGHLIGHTS
	1.1 INTRODUCTION
	1.2 THE CHANGING ROLE OF THE PROCESS OPERATORS IN PLANT OPERATIONS
	1.3 TRADITIONAL METHODS FOR PERFORMING PROCESS FAULT MANAGEMENT
	1.4 LIMITATIONS OF HUMAN OPERATORS IN PERFORMING PROCESS FAULT MANAGEMENT
	1.5 THE ROLE OF AUTOMATED PROCESS FAULT ANALYSIS
	REFERENCES
CHAPTER 2 VARIOUS PROCESS FAULT DIAGNOSTIC METHODOLOGIES
	OVERVIEW
	CHAPTER HIGHLIGHTS
	2.1 INTRODUCTION
	2.2 VARIOUS ALTERNATIVE DIAGNOSTIC STRATEGIES OVERVIEW
		2.2.1 Fault Tree Analysis
		2.2.2 Alarm Analysis
		2.2.3 Decision Tables
		2.2.4 Sign-Directed Graphs
		2.2.5 History-Based Statistical Methods
		2.2.6 Diagnostic Strategies Based upon Qualitative Models
		2.2.7 Diagnostic Strategies Based upon Quantitative Models
		2.2.8 Artificial Neural Network Strategies
		2.2.9 Artificial Immune System Strategies
		2.2.10 Knowledge-Based System Strategies
		2.2.11 Role of the Process Operators in Automated Fault Detection and Diagnosis
	2.3 DIAGNOSTIC METHODOLOGY CHOICE CONCLUSIONS
	REFERENCES
CHAPTER 2.A FAILURE MODES AND EFFECTS ANALYSIS
	2.A.1 Introduction
	2.A.2 FMEA Procedure
	2.A.3 Conclusion
CHAPTER 3 ALARM MANAGEMENT AND FAULT DETECTION
	CHAPTER HIGHLIGHTS
	ABBREVIATIONS USED
	3.1 INTRODUCTION
	3.2 APPLICABLE DEFINITIONS AND GUIDELINES
	3.3 THE ALARM MANAGEMENT LIFE CYCLE
		3.3.1 Introduction
		3.3.2 Life Cycle Model
		3.3.3 Alarm Philosophy
		3.3.4 Alarm Identification
		3.3.5 Alarm Rationalization
		3.3.6 Alarm Design
		3.3.7 Implementation
		3.3.8 Operation
		3.3.9 Maintenance
		3.3.10 Monitoring and Assessment
		3.3.11 Management of Change
		3.3.12 Audit
	3.4 GENERATION OF DIAGNOSTIC INFORMATION
		3.4.1 Introduction
		3.4.2 As Part of the Basic Process Control System
		3.4.3 As a Separate Application
	3.5 PRESENTATION OF THE DIAGNOSTIC INFORMATION
		3.5.1 Introduction
		3.5.2 As Part of the Alarm Text
		3.5.3 As a Link to the Diagnostic Application
		3.5.4 As an Indication in the HMI
	3.6 INFORMATION RATES
		3.6.1 Introduction
		3.6.2 Nuisance Alarms
	REFERENCES
CHAPTER 4 OPERATOR PERFORMANCE: SIMULATION AND AUTOMATION
	CHAPTER HIGHLIGHTS
	4.1 BACKGROUND
	4.2 AUTOMATION
		4.2.1 Smart Alarming
		4.2.2 Safe Park Applications
	4.3 SIMULATION
	4.4 RESEARCH
		4.4.1 Method
		4.4.2 Testing and Results
		4.4.3 Operator Performance
		4.4.4 Implications
	4.5 AI INTEGRATION
		4.5.1 Pattern Recognition
		4.5.2 Training
	4.6 CASE STUDY: TURBO EXPANDERS OVER-SPEED
	4.7 HUMAN-CENTERED AI
		4.7.1 Case Study: Boeing 737 MAX
		4.7.2 AI Mental Models
	REFERENCES
CHAPTER 5 AI AND ALARM ANALYTICS FOR FAILURE ANALYSIS AND PREVENTION
	OVERVIEW
	5.1 INTRODUCTION
	5.2 POST-ALARM ASSESSMENT AND ANALYSIS
		5.2.1 Alarm Configuration Database
	5.3 REAL-TIME ALARM ACTIVITY DATABASE AND OPERATOR ACTION JOURNAL
	5.4 PRE-ALARM ASSESSMENT AND ANALYSIS
	5.5 UTILIZING ALARM ASSESSMENT INFORMATION
	5.6 EXAMINING THE ALARM SYSTEM TO RESOLVE FAILURES ON A WIDER SCALE
		5.6.1 Sequence of Events (SOE) Module
		5.6.2 Use of First Principles to Determine Likely Root Causes
		5.6.3 Use of Simple Data Analytics to Identify Redundant/Repetitive Alarms
		5.6.4 Use of Data Analytics to Identify Problem Areas with Upsets Related to Transitions, out of Service, and out of Suppression States
		5.6.5 Use of Data Analytics to Identify Problem Areas with Chronic Alarm Shelving
	5.7 EMERGING METHODS OF ALARM ANALYSIS
		5.7.1 Use of Advanced Modeling Methods to Determine Remediation
		5.7.2 Use of Automated Machine Learning to Determine Causes and Assess Interventions
	5.8 DEEP REINFORCEMENT LEARNING FOR ALARMING AND FAILURE ASSESSMENT
	5.9 SOME TYPICAL AI AND MACHINE LEARNING EXAMPLES FOR FURTHER STUDY
		5.9.1 Boolean Logic Tables
		5.9.2 Statistical Regression and Variance
		5.9.3 Artificial Neural Networks (ANNs)
		5.9.4 Expert Systems
		5.9.5 Sensitivity Analysis
		5.9.6 Fuzzy Logic
		5.9.7 Bayesian Networks
		5.9.8 Genetic Algorithms
		5.9.9 SmartSignal, PRiSM (AVEVA), and PPCL
		5.9.10 Control System Effectiveness Study
	5.10 WRAP-UP
CHAPTER 5.A PROCESS STATE TRANSITION LOGIC EMPLOYED BY THE ORIGINAL FMC FALCONEER KBS
	5.A.1 INTRODUCTION
	5.A.2 POSSIBLE PROCESS OPERATING STATES
	5.A.3 SIGNIFICANCE OF PROCESS STATE IDENTIFICATION AND TRANSITION DETECTION
	5.A.4 METHODOLOGY FOR DETERMINING PROCESS STATE IDENTIFICATION
		5.A.4.1 Present Value States of all Key Sensor Data
		5.A.4.2 Predicted Next Value States of all Key Sensor Data
	5.A.5 PROCESS STATE IDENTIFICATION AND TRANSITION LOGIC PSEUDO-CODE
		5.A.5.1 Attributes of the Current Data Vector
		5.A.5.2 Method that is Applied to Each Updated Data Vector
	5.A.6 SUMMARY
	APPENDIX 5.A REFERENCES
CHAPTER 5.B PROCESS STATE TRANSITION LOGIC AND ITS ROUTINE USE IN FALCONEER™ IV
	5.B.1 TEMPORAL REASONING PHILOSOPHY
	5.B.2 INTRODUCTION
	5.B.3 STATE IDENTIFICATION ANALYSIS CURRENTLY USED IN FALCONEER™ IV
	5.B.4 STATE IDENTIFICATION ANALYSIS SUMMARY
	APPENDIX 5.B REFERENCES
CHAPTER 6 PROCESS FAULT DETECTION BASED ON TIME-EXPLICIT KIVIAT DIAGRAM
	OVERVIEW
	CHAPTER HIGHLIGHTS
	6.1 INTRODUCTION
	6.2 TIME-EXPLICIT KIVIAT DIAGRAM
	6.3 FAULT DETECTION BASED ON THE TIME-EXPLICIT KIVIAT DIAGRAM
	6.4 CONTINUOUS PROCESSES
	6.5 BATCH PROCESSES
	6.6 PERIODIC PROCESSES
	6.7 CASE STUDIES
	6.8 CONTINUOUS PROCESSES
	6.9 BATCH PROCESSES
	6.10 PERIODIC PROCESSES
	6.11 CONCLUSIONS
	ACKNOWLEDGMENT
	REFERENCES
	ACKNOWLEDGMENTS
	APPENDIX 6.A REFERENCES
CHAPTER 6.A VIRTUAL STATISTICAL PROCESS CONTROL ANALYSIS
	6.A.1 OVERVIEW
	6.A.2 INTRODUCTION
	6.A.3 EWMA CALCULATIONS AND SPECIFIC VIRTUAL SPC ANALYSIS CONFIGURATIONS
		6.A.3.1 Controlled Variables
		6.A.3.2 Uncontrolled Variables and Performance Equation Variables
	6.A.4 VIRTUAL SPC ALARM TRIGGER SUMMARY
	6.A.5 VIRTUAL SPC ANALYSIS CONCLUSIONS
	ACKNOWLEDGMENTS
	APPENDIX 6.A REFERENCES
CHAPTER 7 SMART MANUFACTURING AND REAL-TIME CHEMICAL PROCESS HEALTH MONITORING AND DIAGNOSTIC LOCALIZATION
	CHAPTER HIGHLIGHTS
	7.1 INTRODUCTION TO PROCESS OPERATIONAL HEALTH MODELING
	7.2 DIAGNOSTIC LOCALIZATION – KEY CONCEPTS
		7.2.1 Qualitative Modeling and Symptomaticand Topographic Search
		7.2.2 Functional Representation as a Qualitative Modeling Construct
		7.2.3 Causal Link Assessment for Combined Topographical and Symptomatic Assessment
	7.3 TIME
		7.3.1 Discretization and Single Time-Step Analysis
		7.3.2 Dynamics in an Individual Functional Representation
		7.3.3 Time Window and Feature Extraction
	7.4 THE WORKFLOW OF DIAGNOSTIC LOCALIZATION
	7.5 DL-CLA USE CASE IMPLEMENTATION: NOVA CHEMICAL ETHYLENE SPLITTER
		7.5.1 CPP Generation
		7.5.2 CPP Interpretation
		7.5.3 Diagnostic Localization
	7.6 ANALYZING POTENTIAL MALFUNCTIONS OVER TIME
	7.7 ANALYSIS OF VARIOUS OPERATIONAL SCENARIOS
		7.7.1 Event Manifestation, Sensor Reliability/Sensor Malfunctions
		7.7.2 Hypothetical and Function-Only Devices
		7.7.3 Unaccounted Malfunctions, Graceful Degradation, Multiple Malfunctions, Etc.
		7.7.4 Sensor Availability and Reliability
		7.7.5 Process Complexity
	7.8 DL-CLA INTEGRATION WITH SMART MANUFACTURING (SM)
	7.9 AN FR MODEL LIBRARY
		7.9.1 Reusing FR Device Models
		7.9.2 Complex FR Models
		7.9.3 Analysis and Results
	7.10 CONCLUSIONS
	REFERENCES
CHAPTER 8 OPTIMAL QUANTITATIVE MODEL-BASED PROCESS FAULT DIAGNOSIS
	OVERVIEW
	CHAPTER HIGHLIGHTS
	8.1 INTRODUCTION
	8.2 PROCESS FAULT ANALYSIS CONCEPT TERMINOLOGY
	8.3 MOME QUANTITATIVE MODELS OVERVIEW
	8.4 MOME QUANTITATIVE MODEL DIAGNOSTIC STRATEGY
	8.5 MOME SV&PFA DIAGNOSTIC RULES’ LOGIC COMPILER MOTIVATIONS
	8.6 MOME FUZZY LOGIC ALGORITHM OVERVIEW
		8.6.1 MOME Fuzzy Logic Algorithm Details
		8.6.2 Single Fault Fuzzy Logic SV&PFA Diagnostic Rule
		8.6.3 Multiple Fault Fuzzy Logic SV&PFA Diagnostic Rule
	8.7 SUMMARY OF THE MOME DIAGNOSTIC STRATEGY
	8.8 ACTUAL PROCESS SYSTEM KBS APPLICATION PERFORMANCE RESULTS
	8.9 CONCLUSIONS
	ACKNOWLEDGMENTS
	REFERENCES
CHAPTER 8.A FALCONEERTM IV FUZZY LOGIC ALGORITHM PSEUDO-CODE
	8.A.1 Introduction
	8.A.2 Single and Non-Interactive Multiple Faults
	8.A.3 Pairs of Interactive Multiple Faults
	8.A.4 Summary
CHAPTER 8.B MOME CONCLUSIONS
	8.B.1 Overview
	8.B.2 Summary of the Mome Diagnostic Strategy
	8.B.3 Falconeer™ IV KBS Application Project Procedure
	8.B.4 Optimal Automated Process Fault Analysis Conclusions
CHAPTER 9 FAULT DETECTION USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
	CHAPTER HIGHLIGHTS
	ABBREVIATIONS USED
	9.1 INTRODUCTION
	9.2 ARTIFICIAL INTELLIGENCE
	9.3 MACHINE LEARNING
	9.4 ENGINEERED FEATURES
		9.4.1 Fast Fourier Transformation and Signal Processing
		9.4.2 Principal Component Analysis
	9.5 MACHINE LEARNING ALGORITHMS
		9.5.1 Decision Trees and Ensemble Trees
		9.5.2 Artificial Neural Networks
		9.5.3 Bayesian Networks
		9.5.4 High-Density Clustering
		9.5.5 Large Language Models and the Future AI-Driven Factories
	REFERENCES
CHAPTER 10 KNOWLEDGE-BASED SYSTEMS
	CHAPTER HIGHLIGHTS
	ABBREVIATIONS USED
	10.1 INTRODUCTION
	10.2 KNOWLEDGE
		10.2.1 Definition
		10.2.2 Mathematical – Physical Contextualization
		10.2.3 Procedural Knowledge
		10.2.4 Heuristic Knowledge
		10.2.5 Topological Knowledge
	10.3 INFORMATION REQUIRED FOR DIAGNOSIS
		10.3.1 Introduction
		10.3.2 Association
		10.3.3 State and State Transition
	10.4 KNOWLEDGE REPRESENTATION
		10.4.1 Introduction
		10.4.2 Mathematical
		10.4.3 Linguistic
		10.4.4 Graphical
		10.4.5 Conclusion
	10.5 MAINTAINING, UPDATING, AND EXTENDING KNOWLEDGE
		10.5.1 Introduction
		10.5.2 Progressing Insight
		10.5.3 Software as a Knowledge-Based System
		10.5.4 A Database as a Knowledge-Based System
		10.5.5 Search Engines
		10.5.6 Online Encyclopedia
		10.5.7 Learning
	10.6 EXPERT SYSTEMS
		10.6.1 Introduction
		10.6.2 Inference Engine
		10.6.3 Generic – Specific
		10.6.4 Interactive – Real-Time
		10.6.5 Polling
		10.6.6 Conclusions
		10.6.7 Documentation
	10.7 DIGITIZATION, DIGITALIZATION, DIGITAL TRANSFORMATION, AND DIGITAL TWINS
		10.7.1 Digitizing Information
		10.7.2 Digitalization
		10.7.3 Digital Transformation
		10.7.4 Digital Twins
	10.8 FAULT DIAGNOSIS WITH KNOWLEDGE-BASED SYSTEMS
		10.8.1 Introduction
		10.8.2 Elimination
		10.8.3 Inductive (Forward) Reasoning
		10.8.4 Deductive (Backward) Reasoning
		10.8.5 Conclusions
	10.9 GRAPHICAL REPRESENTATION OF FAULT DIAGNOSIS
		10.9.1 Introduction
		10.9.2 Fault Trees
		10.9.3 FMEA
		10.9.4 Bowtie Analysis
		10.9.5 Complex Event Processing
	10.10 CONCLUSIONS
	REFERENCES
CHAPTER 10.A COMPRESSOR TRIP PREDICTION
CHAPTER 11 THE FALCON PROJECT
	OVERVIEW
	CHAPTER HIGHLIGHTS
	11.1 INTRODUCTION
	11.2 THE DIAGNOSTIC PHILOSOPHY UNDERLYING THE FALCON SYSTEM
	11.3 TARGET PROCESS SYSTEM
	11.4 THE FIELDED FALCON SYSTEM
		11.4.1 The Inference Engine
		11.4.2 The Human/Machine Interface
		11.4.3 The Dynamic Simulation Model
		11.4.4 The Diagnostic Knowledge Base
	11.5 THE DERIVATION OF THE FALCON DIAGNOSTIC KNOWLEDGE BASE
		11.5.1 First Rapid Prototype of the FALCON System KBS
		11.5.2 The Fielded FALCON System Development
		11.5.3 The Fielded FALCON System’s Performance Results
	11.6 THE IDEAL FALCON SYSTEM
	11.7 USE OF THE KNOWLEDGE-BASED SYSTEM PARADIGM IN PROBLEM SOLVING
	ACKNOWLEDGMENTS
	REFERENCES
CHAPTER 12 FAULT DIAGNOSTIC APPLICATION IMPLEMENTATION AND SUSTAINABILITY
	OVERVIEW
	CHAPTER HIGHLIGHTS
	NOMENCLATURE
	12.1 KEY PRINCIPLES OF SUCCESSFULLY IMPLEMENTING NEW TECHNOLOGY
	12.2 EXPECTATION OF ADVANCED TECHNOLOGY
		12.2.1 What Are the Expected Actions?
		12.2.2 Who Is the Audience?
		12.2.3 What Are the Failure Modes?
		12.2.4 When Is an Alert Expected and Valid?
	12.3 DEFINING SUCCESS
	12.4 LEARNING FROM HISTORY
	12.5 EXAMPLE: REGULATORY CONTROL LOOP MONITORING
		12.5.1 Regulatory Control Failure Modes
		12.5.2 Expectations of Loop Monitoring
	12.6 WHAT SUCCESS LOOKS LIKE
	12.7 EXAMPLE: SYSTEMATIC STEWARDSHIP
	12.8 CONCLUSIONS
		12.8.1 Motivational Requirement
		12.8.2 Setup Requirements
		12.8.3 Usage Requirements
		12.8.4 Sustainment and Continuous Improvement
	REFERENCES
CHAPTER 13 PROCESS OPERATORS, ADVANCED PROCESS CONTROL, AND ARTIFICIAL INTELLIGENCE-BASED APPLICATIONS IN THE CONTROL ROOM
	CHAPTER HIGHLIGHTS
	OVERVIEW
	13.1 INTRODUCTION
	13.2 HISTORY OF SUSTAINABLE APC
	13.3 OPERATORS AS ULTIMATE APC APPLICATION END USERS
	13.4 APC APPLICATION DESIGN CONSIDERATIONS
	13.5 APC DEVELOPMENT – INTERNAL VERSUS EXTERNAL EXPERTS
	13.6 APC TECHNOLOGY
	13.7 APC SUPPORT
	13.8 CONCLUSIONS
	REFERENCES
INDEX
EULA