Artificial intelligence in process fault diagnosis : methods for plant surveillance / Richard J. Fickelscherer.

By: Fickelscherer, Richard J [author.]
Language: English Publisher: Hoboken, NJ : Wiley-AIChE, 2024Description: 1 online resource (432 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9781119825890 ; 9781119825920; 111982592XSubject(s): Chemical process control -- Data processing | Fault location (Engineering) -- Data processing | Artificial intelligence -- Industrial applicationsGenre/Form: Electronic books.DDC classification: 660/.281563 LOC classification: TP155.75Online resources: Full text is available at Wiley Online Library Click here to view
Contents:
List of Contributors -- Foreward -- Preface -- Acknowledgements -- 1 Motivations for Automating Process Fault Analysis -- 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 -- 2 Various Process Fault Diagnostic Methodologies -- 2.1 Introduction -- 2.2 Various Alternative Diagnostic Strategies Overview -- 2.3 Diagnostic Methodology Choice Conclusions -- 2.A Failure Modes and Effects Analysis -- 3 Alarm Management and Fault Detection -- 3.1 Introduction -- 3.2 Applicable Definitions and Guidelines -- 3.3 The Alarm Management Life Cycle -- 3.4 Generation of Diagnostic Information -- 3.5 Presentation of the Diagnostic Information -- 3.6 Information Rates -- 4 Operator Performance: Simulation and Automation -- 4.1 Background -- 4.2 Automation -- 4.3 Simulation -- 4.4 Research -- 4.5 AI Integration -- 4.6 Case Study: Turbo Expanders Over-Speed -- 4.7 Human-Centered AI -- 5 AI and Alarm Analytics for Failure Analysis and Prevention -- 5.1 Introduction -- 5.2 Post-Alarm Assessment and Analysis -- 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 -- 5.7 Emerging Methods of Alarm Analysis -- 5.8 Deep Reinforcement Learning for Alarming and Failure Assessment -- 5.9 Some Typical AI and Machine Learning Examples for Further Study -- 5.10 Wrap-Up -- 5.A Process State Transition Logic Employed by the Original FMC Falconeer KBS -- 5.B Process State Transition Logic and its Routine Use in Falconeer IV -- 6 Process Fault Detection Based on Time-Explicit Kiviat Diagram -- 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 -- 6.A Virtual Statistical Process Control Analysis -- 7 Smart Manufacturing and Real-Time Chemical Process Health Monitoring and Diagnostic Localization -- 7.1 Introduction to Process Operational Health Modeling -- 7.2 Diagnostic Localization - Key Concepts -- 7.3 Time -- 7.4 The Workflow of Diagnostic Localization -- 7.5 DL-CLA Use Case Implementation: Nova Chemical Ethylene Splitter -- 7.6 Analyzing Potential Malfunctions Over Time -- 7.7 Analysis of Various Operational Scenarios -- 7.8 DL-CLA Integration with Smart Manufacturing (SM) -- 7.9 AN FR Model Library -- 7.10 Conclusions -- 8 Optimal Quantitative Model-Based Process Fault Diagnosis -- 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 -- 8.7 Summary of the Mome Diagnostic Strategy -- 8.8 Actual Process System KBS Application Performance Results -- 8.9 Conclusions -- 8.A Falconeer IV Fuzzy Logic Algorithm Pseudo-Code -- 8.B Mome Conclusions -- 9 Fault Detection Using Artificial Intelligence and Machine Learning -- 9.1 Introduction -- 9.2 Artificial Intelligence -- 9.3 Machine Learning -- 9.4 Engineered Features -- 9.5 Machine Learning Algorithms -- 10 Knowledge-Based Systems -- 10.1 Introduction -- 10.2 Knowledge -- 10.3 Information Required for Diagnosis -- 10.4 Knowledge Representation -- 10.5 Maintaining, Updating, and Extending Knowledge -- 10.6 Expert Systems -- 10.7 Digitization, Digitalization, Digital Transformation, and Digital Twins -- 10.8 Fault Diagnosis with Knowledge-Based Systems -- 10.9 Graphical Representation of Fault Diagnosis -- 10.10 Conclusions -- 10.A Compressor Trip Prediction -- 11 The Falcon Project -- 11.1 Introduction -- 11.2 The Diagnostic Philosophy Underlying the Falcon System -- 11.3 Target Process System -- 11.4 The Fielded Falcon System -- 11.5 The Derivation of the FALCON Diagnostic Knowledge Base -- 11.6 The Ideal FALCON System -- 11.7 Use of the Knowledge-Based System Paradigm in Problem -- 12 Fault Diagnostic Application Implementation and Sustainability -- 12.1 Key Principles of Successfully Implementing New Technology -- 12.2 Expectation of Advanced Technology -- 12.3 Defining Success -- 12.4 Learning from History -- 12.5 Example: Regulatory Control Loop Monitoring -- 12.6 What Success Looks Like -- 12.7 Example: Systematic Stewardship -- 12.8 Conclusions -- 13 Process Operators, Advanced Process Control, and Artificial Intelligence-Based Applications in the Control Room -- 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.
Summary: Artificial Intelligence in Process Fault Diagnosis A comprehensive guide to the future of process fault diagnosis Automation has revolutionized every aspect of industrial production, from the accumulation of raw materials to quality control inspections. Even process analysis itself has become subject to automated efficiencies, in the form of process fault analyzers, i.e., computer programs capable of analyzing process plant operations to identify faults, improve safety, and enhance productivity. Prohibitive cost and challenges of application have prevented widespread industry adoption of this technology, but recent advances in artificial intelligence promise to place these programs at the center of manufacturing process analysis. Artificial Intelligence in Process Fault Diagnosis brings together insights from data science and machine learning to deliver an effective introduction to these advances and their potential applications. Balancing theory and practice, it walks readers through the process of choosing an ideal diagnostic methodology and the creation of intelligent computer programs. The result promises to place readers at the forefront of this revolution in manufacturing. Artificial Intelligence in Process Fault Diagnosis readers will also find: Coverage of various AI-based diagnostic methodologies elaborated by leading experts Guidance for creating programs that can prevent catastrophic operating disasters, reduce downtime after emergency process shutdowns, and more Comprehensive overview of optimized best practices Artificial Intelligence in Process Fault Diagnosis is ideal for process control engineers, operating engineers working with processing industrial plants, and plant managers and operators throughout the various process industries.
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Includes bibliographical references and index.

List of Contributors -- Foreward -- Preface -- Acknowledgements -- 1 Motivations for Automating Process Fault Analysis -- 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 -- 2 Various Process Fault Diagnostic Methodologies -- 2.1 Introduction -- 2.2 Various Alternative Diagnostic Strategies Overview -- 2.3 Diagnostic Methodology Choice Conclusions -- 2.A Failure Modes and Effects Analysis -- 3 Alarm Management and Fault Detection -- 3.1 Introduction -- 3.2 Applicable Definitions and Guidelines -- 3.3 The Alarm Management Life Cycle -- 3.4 Generation of Diagnostic Information -- 3.5 Presentation of the Diagnostic Information -- 3.6 Information Rates -- 4 Operator Performance: Simulation and Automation -- 4.1 Background -- 4.2 Automation -- 4.3 Simulation -- 4.4 Research -- 4.5 AI Integration -- 4.6 Case Study: Turbo Expanders Over-Speed -- 4.7 Human-Centered AI -- 5 AI and Alarm Analytics for Failure Analysis and Prevention -- 5.1 Introduction -- 5.2 Post-Alarm Assessment and Analysis -- 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 -- 5.7 Emerging Methods of Alarm Analysis -- 5.8 Deep Reinforcement Learning for Alarming and Failure Assessment -- 5.9 Some Typical AI and Machine Learning Examples for Further Study -- 5.10 Wrap-Up -- 5.A Process State Transition Logic Employed by the Original FMC Falconeer KBS -- 5.B Process State Transition Logic and its Routine Use in Falconeer IV -- 6 Process Fault Detection Based on Time-Explicit Kiviat Diagram -- 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 -- 6.A Virtual Statistical Process Control Analysis -- 7 Smart Manufacturing and Real-Time Chemical Process Health Monitoring and Diagnostic Localization -- 7.1 Introduction to Process Operational Health Modeling -- 7.2 Diagnostic Localization - Key Concepts -- 7.3 Time -- 7.4 The Workflow of Diagnostic Localization -- 7.5 DL-CLA Use Case Implementation: Nova Chemical Ethylene Splitter -- 7.6 Analyzing Potential Malfunctions Over Time -- 7.7 Analysis of Various Operational Scenarios -- 7.8 DL-CLA Integration with Smart Manufacturing (SM) -- 7.9 AN FR Model Library -- 7.10 Conclusions -- 8 Optimal Quantitative Model-Based Process Fault Diagnosis -- 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 -- 8.7 Summary of the Mome Diagnostic Strategy -- 8.8 Actual Process System KBS Application Performance Results -- 8.9 Conclusions -- 8.A Falconeer IV Fuzzy Logic Algorithm Pseudo-Code -- 8.B Mome Conclusions -- 9 Fault Detection Using Artificial Intelligence and Machine Learning -- 9.1 Introduction -- 9.2 Artificial Intelligence -- 9.3 Machine Learning -- 9.4 Engineered Features -- 9.5 Machine Learning Algorithms -- 10 Knowledge-Based Systems -- 10.1 Introduction -- 10.2 Knowledge -- 10.3 Information Required for Diagnosis -- 10.4 Knowledge Representation -- 10.5 Maintaining, Updating, and Extending Knowledge -- 10.6 Expert Systems -- 10.7 Digitization, Digitalization, Digital Transformation, and Digital Twins -- 10.8 Fault Diagnosis with Knowledge-Based Systems -- 10.9 Graphical Representation of Fault Diagnosis -- 10.10 Conclusions -- 10.A Compressor Trip Prediction -- 11 The Falcon Project -- 11.1 Introduction -- 11.2 The Diagnostic Philosophy Underlying the Falcon System -- 11.3 Target Process System -- 11.4 The Fielded Falcon System -- 11.5 The Derivation of the FALCON Diagnostic Knowledge Base -- 11.6 The Ideal FALCON System -- 11.7 Use of the Knowledge-Based System Paradigm in Problem -- 12 Fault Diagnostic Application Implementation and Sustainability -- 12.1 Key Principles of Successfully Implementing New Technology -- 12.2 Expectation of Advanced Technology -- 12.3 Defining Success -- 12.4 Learning from History -- 12.5 Example: Regulatory Control Loop Monitoring -- 12.6 What Success Looks Like -- 12.7 Example: Systematic Stewardship -- 12.8 Conclusions -- 13 Process Operators, Advanced Process Control, and Artificial Intelligence-Based Applications in the Control Room -- 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.

Available to OhioLINK libraries.

Artificial Intelligence in Process Fault Diagnosis A comprehensive guide to the future of process fault diagnosis Automation has revolutionized every aspect of industrial production, from the accumulation of raw materials to quality control inspections. Even process analysis itself has become subject to automated efficiencies, in the form of process fault analyzers, i.e., computer programs capable of analyzing process plant operations to identify faults, improve safety, and enhance productivity. Prohibitive cost and challenges of application have prevented widespread industry adoption of this technology, but recent advances in artificial intelligence promise to place these programs at the center of manufacturing process analysis. Artificial Intelligence in Process Fault Diagnosis brings together insights from data science and machine learning to deliver an effective introduction to these advances and their potential applications. Balancing theory and practice, it walks readers through the process of choosing an ideal diagnostic methodology and the creation of intelligent computer programs. The result promises to place readers at the forefront of this revolution in manufacturing. Artificial Intelligence in Process Fault Diagnosis readers will also find: Coverage of various AI-based diagnostic methodologies elaborated by leading experts Guidance for creating programs that can prevent catastrophic operating disasters, reduce downtime after emergency process shutdowns, and more Comprehensive overview of optimized best practices Artificial Intelligence in Process Fault Diagnosis is ideal for process control engineers, operating engineers working with processing industrial plants, and plant managers and operators throughout the various process industries.

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