Optimal Automated Process Fault Analysis

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  • Format: Hardcover
  • Copyright: 2013-01-04
  • Publisher: Wiley-AIChE
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The book first describes motivations for automating process fault analysis in detail and subsequently discusses MOME and its associated Fuzzy logic algorithm. Other chapters cover various topics related to process fault Analysis including the need for augmenting process fault analysis with trend analysis of the various process sensor measurements and Key Performance Indicators (KPIs). Also included is a brief review of a number of the other various possible diagnostic strategies used to automate process fault analysis as well as their limitations. A description of DuPont's plant and the original automated process fault analyzer developed for it including the lessons learned from the development of this real-world fault analyzer and the advantages of using the knowledge-based system paradigm for solving problems are also included.

Table of Contents


Table of Contents




Chapter 1. Motivations for Automating Process Fault Analysis

1.1  Introduction

1.2  CPI Trends to Date

1.3  The Changing Role for the Process Operators in Plant Operations

1.4  Methods Currently Used to Perform Process Fault Management

1.5  Limitations of Human Operators in Performing Process Fault Management

1.6  The Role of Automated Process Fault Analysis

1.7  Anticipated Future CPI Trends

1.8  Process Fault Analysis Concept Terminology

Chapter 2. Method of Minimal Evidence: Model-Based Reasoning

2.1 Overview

2.2 Introduction

2.3 Method of Minimal Evidence Overview

2.4 Verifying the Validity and Accuracy of the Various Primary Models

2.5 Summary

Chapter 3. Method of Minimal Evidence: Diagnostic Strategy Details

3.1 Overview

3.2 Introduction

3.3 MOME Diagnostic Strategy

3.4 A General Procedure for Developing and Verifying Competent Model-based

3.5 MOME SV & PFA Diagnostic Logic Compiler Motivations

3.6 MOME Diagnostic Strategy Summary

Chapter 4. Method of Minimal Evidence: Fuzzy Logic Algorithm

4.1 Overview

4.2 Introduction

4.3 Fuzzy Logic Overview

4.4 MOME Fuzzy Logic Algorithm

4.5 Certainty Factor Calculation Review

4.6 MOME Fuzzy Logic Algorithm Summary

Chapter 5. Method of Minimal Evidence: Criteria for Shrewdly Distribution Fault Analyzers and Strategic Process Sensor Placement

5.1 Overview

5.2 Criteria for Shrewdly Distributing Process Fault Analyzers

5.3 Criteria for Strategic Process Sensor Placement

Chapter 6. Virtual SPC Analysis and Its Routine Use in Falconeer™ IV

6.1 Overview

6.2 Introduction

6.3 EWMA Calculations and Specific Virtual SPC Analysis Configurations

6.4 Virtual SPC Alarm Trigger Summary

6.5 Virtual SPC Analysis Conclusions

Chapter 7. Process State Transistion Logic and Its Routine Use in Falconeer™ IV

7.1 Temporal Reasoning Philosophy

7.2 Introduction

7.3 State Identification Analysis Currently Used in Falconeer™ IV

7.4 State Identification Analysis Summary

Chapter 8. Conclusions

8.1 Overview

8.2 Summary of the MOME Diagnostic Strategy

8.3 FALCON, FALCONEER and FALCONEER™ IV Actual KBS Application Performance Results

8.4 FALCONEER™ IV KBS Application Project Procedure

8.5 Optimal Automated Process Fault Analysis Conclusions

Appendix A. Various Diagnostic Strategies for Automating Process Fault Analysis

Appendix B. The Falcon Project

Appendix C. Process State Transition Logic Used by the Original Falconeer KBS

Appendix D. Falconeer™ IV Real-Time Suite Process Performance Solutions Demo Description

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