SUPERVISION SYSTEMS

0. INDEX

1	Introduction.
2	Foundations of process supervision.
3	Analytical model-based monitoring, supervision and fault detection and isolation methods.
4	Foundations of artificial intelligence for knowledge representation and processing: Selected methods.
5	Computer aided supervisory system design: An overview.
6	From data to knowledge: Signal to symbol transformation.
7	Knowledge-based process monitoring, supervision, decision support and diagnosis: Selected methodologies.
8	Case studies and example problems.
9	Tools for monitoring, supervision and diagnosis.
10	Benchmarks, open problems, research directions.
11	Source materials and references. A guide.

1. INTRODUCTION

1.1 Introduction to process supervision

1.2 CAD-type tools: modern trends

1.3 SCADA systems and expert supervision: an overview

1.4 Fault detection: structures and methodologies

1.5 Knowledge-based fault diagnosis

1.6 IFAC definitions

2. FOUNDATIONS OF PROCESS SUPERVISION

2.1 Definition, tasks, and model of process supervision

2.2 Basic configurations

2.3 Pre-processing of numerical data

2.4 Multiple-level model of supervision

2.5 Mathematical models and methods in process supervision

2.6 Typical schemes and applications of supervisory systems

3. ANALYTICAL MODEL-BASED MONITORING, SUPERVISION AND FAULT DETECTION AND ISOLATION METHODS

4. FOUNDATIONS OF ARTIFICIAL INTELLIGENCE FOR KNOWLEDGE REPRESENTATION AND PROCESSING: SELECTED METHODS

4.1 Motivation of the use of AI methods in process supervision

4.2 Types of data

4.3 Symbolic data representation

4.4 Qualitative knowledge representation

4.5 Knowledge representation with logic

4.6 Decision lists, decision tables, decision trees

4.7 Knowledge representation with fuzzy logic

4.8 Rule-based systems

4.9 Fuzzy rule-based systems

4.10 Expert systems

4.11 Graphs and causal graphs

4.12 Representation of knowledge over time: episodes

4.13 Case-based reasoning

4.14 A note on other forms of knowledge representation

4.15 Generic problems to be solved for developing a knowledge-based system

4.16 Artificial Intelligence in control systems

4.17 Artificial Intelligence in process monitoring and supervision

5. COMPUTER AIDED SUPERVISORY SYSTEM DESIGN: AN OVERVIEW

6. FROM DATA TO KNOWLEDGE: SIGNAL TO SYMBOL TRANSFORMATION

6.1 Steps from data to knowledge

6.2 States and situations

6.3 Formal foundations of knowledge formation

6.4 Numeric to symbolic translation

6.5 Signals representation by means of episodes

6.6 A formalism for episodes-based representations

6.7 A representation based on the formalism

6.8 Data and knowledge representation with object variables

6.9 Data abstraction tools

7. KNOWLEDGE-BASED PROCESS MONITORING, SUPERVISION, DECISION SUPPORT AND DIAGNOSIS: SELECTED METHODOLOGIES

7.1 Knowledge-based supervision: selected issues

7.2 Signal recognition and monitoring with use of episodes

7.3 Advanced techniques in signal analysis with episodes

7.4 Qualitative signal analysis: ALCMEN

7.5 Rule-based systems, decision tables, and decision trees in state monitoring and situation classification control and decision support

7.6 Object oriented methodologies and expert systems: CEES

7.7 An integrated environment for CASSD

7.8 Model based supervision

7.9 Case-based reasoning in process supervision and diagnosis – selected issues

7.10 Simple diagnostic models for fault identification

7.11 Causal logical graphs for diagnostic reasoning

7.12 Diagnostics

7.13 Fuzzy logic in process supervision

8. CASE STUDIES AND EXAMPLE PROBLEMS

8.1 Application of episodes and expert system for supervision and diagnosis of the three tank model system

8.2 Case study of episode application to supervision of systems

8.3 The furnace example

8.4 Problems of knowledge based validation

8.5 Manufacturing process supervision

8.6 A simple diagnostic example

8.7 Other case-studies: references

9. TOOLS FOR MONITORING, SUPERVISION AND DIAGNOSIS

9.1 SCADA systems

9.2 Rule-based systems and expert system shells

9.3 Episode generating environments

9.4 CASSD-type environments

9.5 TIGER: an example of complex system

9.6 9.6 Diagnostic systems

10. BENCHMARKS, OPEN PROBLEMS, RESEARCH DIRECTIONS

10.1 The three tanks benchmark

10.2 The Frank/Patton benchmark

10.3 The mono-stable benchmark

10.4 Research directions and selected open problems

11. SOURCE MATERIALS AND REFERENCES. A GUIDE

11.1 Glossary

11.2 Recommended readings

11.3 Selected Internet Sites

11.4 References

1.1	Introduction to process supervision
1.2	CAD-type tools: modern trends
1.3	SCADA systems and expert supervision: an overview
1.4	Fault detection: structures and methodologies
1.5	Knowledge-based fault diagnosis
1.6	IFAC definitions

2.1	Definition, tasks, and model of process supervision
2.2	Basic configurations
2.3	Pre-processing of numerical data
2.4	Multiple-level model of supervision
2.5	Mathematical models and methods in process supervision
2.6	Typical schemes and applications of supervisory systems

4.1	Motivation of the use of AI methods in process supervision
4.2	Types of data
4.3	Symbolic data representation
4.4	Qualitative knowledge representation
4.5	Knowledge representation with logic
4.6	Decision lists, decision tables, decision trees
4.7	Knowledge representation with fuzzy logic
4.8	Rule-based systems
4.9	Fuzzy rule-based systems
4.10	Expert systems
4.11	Graphs and causal graphs
4.12	Representation of knowledge over time: episodes
4.13	Case-based reasoning
4.14	A note on other forms of knowledge representation
4.15	Generic problems to be solved for developing a knowledge-based system
4.16	Artificial Intelligence in control systems
4.17	Artificial Intelligence in process monitoring and supervision

6.1	Steps from data to knowledge
6.2	States and situations
6.3	Formal foundations of knowledge formation
6.4	Numeric to symbolic translation
6.5	Signals representation by means of episodes
6.6	A formalism for episodes-based representations
6.7	A representation based on the formalism
6.8	Data and knowledge representation with object variables
6.9	Data abstraction tools

7.1	Knowledge-based supervision: selected issues
7.2	Signal recognition and monitoring with use of episodes
7.3	Advanced techniques in signal analysis with episodes
7.4	Qualitative signal analysis: ALCMEN
7.5	Rule-based systems, decision tables, and decision trees in state monitoring and situation classification control and decision support
7.6	Object oriented methodologies and expert systems: CEES
7.7	An integrated environment for CASSD
7.8	Model based supervision
7.9	Case-based reasoning in process supervision and diagnosis – selected issues
7.10	Simple diagnostic models for fault identification
7.11	Causal logical graphs for diagnostic reasoning
7.12	Diagnostics
7.13	Fuzzy logic in process supervision

8.1	Application of episodes and expert system for supervision and diagnosis of the three tank model system
8.2	Case study of episode application to supervision of systems
8.3	The furnace example
8.4	Problems of knowledge based validation
8.5	Manufacturing process supervision
8.6	A simple diagnostic example
8.7	Other case-studies: references

9.1	SCADA systems
9.2	Rule-based systems and expert system shells
9.3	Episode generating environments
9.4	CASSD-type environments
9.5	TIGER: an example of complex system
9.6	9.6 Diagnostic systems

10.1	The three tanks benchmark
10.2	The Frank/Patton benchmark
10.3	The mono-stable benchmark
10.4	Research directions and selected open problems

11.1	Glossary
11.2	Recommended readings
11.3	Selected Internet Sites
11.4	References