Artificial Intelligence in Process Fault Diagnosis: Methods for Plant Surveillance
- 8h 3m
- Richard J. Fickelscherer
- John Wiley & Sons (US)
- 2024
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.
About the Author
Richard J. Fickelscherer, PhD, PE has worked on advanced process control and process monitoring programs at DuPont, Exxon, Merck Pharmaceuticals, Koch Industries, and FMC, and has since developed and patented a Fuzzy logic-based compiler program to automate process fault analysis.
In this Book
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Foreword
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Motivations for Automating Process Fault Analysis
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Various Process Fault Diagnostic Methodologies
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Failure Modes and Effects Analysis
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Alarm Management and Fault Detection
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Operator Performance: Simulation and Automation
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AI and Alarm Analytics for Failure Analysis and Prevention
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Process State Transition Logic Employed by The Original FMC Falconeer KBS
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Process State Transition Logic and Its Routine Use In Falconeer™ IV
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Process Fault Detection Based on Time‐Explicit Kiviat Diagram
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Virtual Statistical Process Control Analysis
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Smart Manufacturing and Real‐Time Chemical Process Health Monitoring and Diagnostic Localization
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Optimal Quantitative Model‐Based Process Fault Diagnosis
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Falconeer™ IV Fuzzy Logic Algorithm Pseudo‐Code
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Mome Conclusions
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Fault Detection Using Artificial Intelligence and Machine Learning
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Knowledge‐Based Systems
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Compressor Trip Prediction
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The Falcon Project
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Fault Diagnostic Application Implementation and Sustainability
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Process Operators, Advanced Process Control, and Artificial Intelligence‐Based Applications In the Control Room
