eBook - Artificial Intelligence in Process Fault Diagnosis : Methods for Plant Surveillance

  • ISBN: 9789357465632
  • 432 pages

Available Exclusively as an eBook | Part of the AI eBook Collection | Only for Institutional Purchase | Publication Year: 2024

Description

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.

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.

Table of Contents

1 Motivations for Automating Process Fault Analysis

2 Various Process Fault Diagnostic Methodologies

3 Alarm Management and Fault Detection

4 Operator Performance: Simulation and Automation

5 AI and Alarm Analytics for Failure Analysis and Prevention

6 Process Fault Detection Based on Time-Explicit Kiviat Diagram

7 Smart Manufacturing and Real-Time Chemical Process Health Monitoring and Diagnostic Localization

8 Optimal Quantitative Model-Based Process Fault Diagnosis

9 Fault Detection Using Artificial Intelligence and Machine Learning

10 Knowledge-Based Systems

11 The Falcon Project

12 Fault Diagnostic Application Implementation and Sustainability

13 Process Operators, Advanced Process Control, and Artificial Intelligence-Based Applications in the Control Room

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