Expert Systems in Engineering Applications

ISBN-13: 9783642840500 / Angielski / Miękka / 2011 / 383 str.

Spyros Tzafestas

Opis

Expert system technology is receiving increasing popularity and acceptance in the engineering community. This is due to the fact that there actually exists a close match between the capabilities of the current generation expert systems and the requirements of engineering practice. Prepared by a distinguished team of experts, this book provides a balanced state-of-the-art presentation of the design principles of engineering expert systems, and a representative picture of their capabilities to assist efficiently the design, diagnosis and operation of complex industrial plants. Among the application areas covered are the following: hardware synthesis, industrial plant layout design, fault diagnosis, process control, image analysis, computer communication, electric power systems, intelligent control, robotics, and manufacturing systems. The book is appropriate for the researcher and the professional. The researcher can save considerable time in searching the scattered technical information on engineering expert systems. The professional can have readily available a rich set of guidelines and techniques that are applicable to a wide class of engineering domains.

Spis treści

I General Issues.- 1 An Overview of Expert Systems.- 1.1 Introduction.- 1.2 Foundations of Expert Systems.- 1.3 Expert System Architecture.- 1.4 Knowledge Representation in Expert Systems.- 1.4.1 Rule-Based Systems.- 1.4.2 Frame-Based Systems.- 1.4.3 Associative Networks.- 1.4.4 Logic-Based Systems.- 1.4.5 Object-Oriented Systems.- 1.4.6 Attribute Grammar Systems.- 1.5 Comparison of Knowledge Representation Methods.- 1.6 Knowledge Representation in Engineering-Based Expert Systems.- 1.7 Conclusion.- 1.8 Bibliography.- 2 Knowledge Acquisition for Expert System Design.- 2.1 Introduction.- 2.2 Human Experts and Expert Systems.- 2.2.1 Human Experts.- 2.2.2 Expert Systems.- 2.3 The Knowledge Acquisition Problem.- 2.3.1 General Issues.- 2.3.2 The Knowledge Engineer.- 2.4 Knowledge Acquisition Techniques.- 2.4.1 Introductory Aspects.- 2.4.2 The Interview Technique.- 2.4.3 Focused Meetings and Discussions.- 2.4.4 Other Techniques.- 2.5 Application Examples.- 2.5.1 Knowledge Acquisition in Industrial Diagnostic Systems.- 2.5.2 Control Algorithm Acquisition.- 2.5.3 Knowledge Acquisition for a Budgetary Expert System.- 2.6 Conclusions.- 2.7 Bibliography.- 3 Synergy of Expert Systems, CAD, and Logic Programming.- 3.1 Introduction.- 3.1.1 A Circuit Pack Troubleshooting Expert System.- 3.1.2 CAD in Expert Systems.- 3.1.3 AI in CAD.- 3.1.4 Outline.- 3.2 Computers-Aided Design of Circuit Pack Troubleshooting Knowledge Bases.- 3.2.1 Previous Works.- 3.2.2 Graphical Interaction.- 3.2.3 The Graphic Interface.- 3.3 Heuristic Search in the Computer-Aided Design of Diagnostic Expert Systems.- 3.3.1 Previous Works.- 3.3.2 Goal Selection.- 3.4 Reification of Expert Systems.- 3.4.1 Automated Programming Systems.- 3.4.2 Reifying = Instantiation + Inference + Translation.- 3.4.3 ESR Architecture.- 3.4.4 Parse Tree.- 3.5 Summary.- 3.6 Bibliography.- II Expert Systems in Engineering Domains.- 4 Expert Systems for Automatic Hardware Synthesis.- 4.1 Introduction.- 4.2 Selected Expert Systems.- 4.3 A Prototype Expert System.- 4.3.1 The Knowledge Base (KB).- 4.3.2 Implementation.- 4.3.3 Results.- 4.4 Extensions.- 4.4.1 AST to IEI Translator.- 4.4.2 The Knowledge Base.- 4.4.3 The Inference Engine.- 4.5 Summary.- 4.6 Bibliography.- 4.7 Appendix.- 5 Expert System of Image Processing and its Application to LANDSAT Image Analysis.- 5.1 Introduction.- 5.2 Knowledge for Image Processing Expert Systems.- 5.3 Overview of ELIA.- 5.3.1 Features of LANDSAT TM Images.- 5.3.2 Configuration of ELIA.- 5.4 Expert System Modules.- 5.4.1 Initial Segmentation.- 5.4.2 Extraction of Waters.- 5.4.3 Road Extraction.- 5.5 Description of Analyzed Data.- 5.5.1 Basic Concept.- 5.5.2 Graph Modification Rules.- 5.6 Experimental Result.- 5.7 Concluding Remarks.- 5.8 Bibliography.- 6 Self-Learning Expert System for Computer Communication Design.- 6.1 Introduction.- 6.2 Notations and Definitions.- 6.2.1 A Graph.- 6.2.2 Non-Oriented Graph.- 6.2.3 Degree of a Graph.- 6.2.4 Network.- 6.2.5 Disjoint Paths Between Nodes.- 6.2.6 Connectivity of the Nodes.- 6.3 The Design Problem.- 6.3.1 Formulation of the Problem.- 6.3.2 Nature and Complexity of the Problem.- 6.3.3 Classical Methods of Resolution.- 6.4 Artificial Intelligence Approach.- 6.4.1 The Architecture of an Expert System.- 6.4.2 Knowledge Representation.- 6.4.3 Knowledge Acquisition and Self-Learning Mechanisms.- 6.5 Design of the Problem-Solving System.- 6.5.1 Principles of the Proposed Method.- 6.5.2 General Organization of the Proposed System.- 6.5.3 Feasibility and Goodness of Solutions.- 6.6 Bibliography.- 7 Application of Artificial Intelligence and Expert Systems in Power Engineering.- 7.1 Introduction.- 7.2 Fault Diagnosis.- 7.2.1 High-Voltage Network Systems.- 7.2.2 Distribution Network Systems.- 7.2.3 Turbine Generators.- 7.3 Power System Control.- 7.3.1 Reactive Power and Voltage Control.- 7.3.2 Blackout Restoration.- 7.3.3 Determination of Load Block Composition in Under-Frequency Load-Shedding Schemes.- 7.3.4 Generator Scheduling.- 7.4 Load and Circuit Allocations.- 7.4.1 Load Allocation in Distribution Substations.- 7.4.2 Circuit Allocation in Subtransmission Switching Substations.- 7.5 Power System Planning.- 7.5.1 Load Flow Planning.- 7.5.2 Generation Expansion Planning.- 7.5.3 Distribution Expansion Planning.- 7.6 Summary and Conclusions.- 7.6.1 Summary of Work to Date.- 7.6.2 Current and Future Trends.- 7.6.3 Future Research and Development Themes.- 7.7 Bibliography.- 8 A Knowledge-Based Approach to Verification and Improvement of Industrial Plan Layout Design.- 8.1 Introduction.- 8.2 Verification Method.- 8.2.1 Problem Definition.- 8.2.2 Configuration of Method.- 8.3 Improvement Method.- 8.3.1 Problem Definition.- 8.3.2 Configuration of Method.- 8.3.3 Optimal Placement.- 8.4 Results and Evaluations.- 8.5 Conclusions.- 8.6 Bibliography.- III Expert Systems in Fault Diagnosis and Process Control.- 9 EXACT — an Expert System for Automobile Air-Conditioner Compressor Troubleshooting.- 9.1 Introduction.- 9.2 Expert Systems for Troubleshooting.- 9.3 System Development.- 9.3.1 Development of the Knowledge Base.- 9.3.2 Development of the Data Base.- 9.3.3 Development of the Inference Engine.- 9.3.4 Development of the Explanation Module.- 9.4 System Configuration.- 9.5 System Performance Validation.- 9.5.1 Validation Procedure.- 9.5.2 Performance Validation Results.- 9.6 Conclusion.- 9.7 Bibliography.- 9.8 Appendix: An Example of Case Study of EXACT.- 10 Using Prototypical Knowledge in Classification-Based Expert Systems.- 10.1 Introduction.- 10.2 Vibration-Based Monitoring.- 10.3 Overview of DIVA.- 10.3.1 The Situation Recognition Task.- 10.3.2 The Diagnosis Task.- 10.3.3 The Information Retrieval and Data Abstraction Task.- 10.4 Recognition of Typical Situations.- 10.4.1 Description of a Prototype.- 10.4.2 Establish/Refine.- 10.5 Discussion of the Prototype Model.- 10.5.1 Context-Dependent Reasoning.- 10.5.2 System Reliability.- 10.5.3 Knowledge Acquisition.- 10.6 Conclusion.- 10.7 Bibliography.- 11 Combined Control and Diagnosis for Complex Processes: An Intelligent Control Approach.- 11.1 Introduction.- 11.2 Intelligent Control of Complex Processes.- 11.2.1 The Challenge.- 11.2.2 Integrating Control and Diagnosis.- 11.2.3 The Shallow Knowledge Approach: Structure and a Prototype.- 11.2.4 Towards Deep Knowledge: Qualitative Simulation.- 11.2.5 The Semantic Control Approach.- 11.3 An Implementation.- 11.3.1 Program Structure.- 11.3.2 Programming the Finite Automaton in Logic.- 11.3.3 Object-oriented Implementation of the Forward Chainer.- 11.4 Discussion.- 11.5 Bibliography.- 12 Knowledge-Based Adaptive Identification for Process Control and Modelling.- 12.1 Introduction.- 12.2 Quantitative Identification.- 12.3 Intelligent Adaptive Identification.- 12.3.1 Qualitative Identification.- 12.3.2 Intelligent Adaptive Identification.- 12.4 Implementation and Discussion.- 12.5 On-Going Study and Conclusion.- 12.5.1 Real Time Intelligent Control.- 12.5.2 Large-Scale Integrated Intelligent Control.- 12.6 Bibliography.- IV Expert Systems in Robotics and Manufacturing.- 13 Knowledge Based (Expert) Systems for Intelligent Control Applications.- 13.1 Introduction.- 13.2 Knowledge Representation Techniques.- 13.3 Expert Systems in Intelligent Robotics.- 13.4 Expert Systems in Control.- 13.5 Conclusion.- 13.6 Bibliography.- 14 Learning Expert System for Robot Skills.- 14.1 Introduction.- 14.2 EARSA: A Paradigm for Robot Skill Acquisition.- 14.3 Robot Fine Motion Skills.- 14.4 Skill Transfer.- 14.4.1 Encoding of Expert Skills.- 14.4.2 Representation of Robot Skills.- 14.4.3 Interpretation and Operationalization.- 14.5 Robot Self-Learning of Fine Motion Skills.- 14.5.1 Critic and Sample Collection.- 14.5.2 Hypothesis Generation.- 14.5.3 Hypothesis and Rule Monitoring.- 14.6 Simulation.- 14.6.1 Model.- 14.6.2 Test Results.- 14.7 Conclusion.- 14.8 Bibliography.- 15 A Knowledge-Based Mechanical Assembly Planning System.- 15.1 Introduction.- 15.2 Planning and Mechanical Assembly.- 15.3 System Overview.- 15.4 Knowledge Base.- 15.4.1 Workpiece Structures.- 15.4.2 Assembly Principles.- 15.4.3 Assembly Operations.- 15.5 Control Structure.- 15.5.1 Structure Analysis.- 15.5.2 Plan Generation.- 15.6 Conclusion.- 15.7 Bibliography.- 16 Expert Systems in Manufacturing.- 16.1 Introduction.- 16.2 The Importance of Topology.- 16.3 Representation of Shape.- 16.4 Language.- 16.5 Form Features.- 16.6 Extraction of Form Features.- 16.7 Design for Quality.- 16.8 Integration of Structure and Robot.- 16.9 Summary.- 16.10 Bibliography.- 17 Artificial Intelligence Concepts and Petri Nets for Modelling Simulation and Control of Flexible Manufacturing Systems.- 17.1 Introduction.- 17.2 Part I: Petri Net in Manufacturing.- 17.2.1 Illustrative Example: A Flexible Manufacturing System.- 17.2.2 Ordinary Petri Nets (PN).- 17.2.3 Structured Petri Nets (SP-N).- 17.2.4 Petri Net Control Model of the Illustrative Example.- 17.2.5 Conclusion Part I.- 17.3 Part II: A.I. Approach: The Need to Obtain an Efficient Description of the Control Model at High Level of Decision and to Describe the Correct Behaviour of the Process.- 17.3.1 A.I. Modelling of the High Level of Control.- 17.3.2 Object-Oriented Description of the Process Model.- 17.4 Bibliography.- 18 Expert Control Architectures for Production Planning and Control.- 18.1 Introduction.- 18.2 The PP-Problem Statement.- 18.3 The PP-Problem Complexity.- 18.4 The APP-Problem Solution.- 18.4.1 Decomposing the APP-Problem.- 18.4.2 Developing the APP-Strategy.- 18.4.3 Organizing the Hybrid PP-Architecture.- 18.5 Discussing a Simple Example.- 18.6 Analyzing the HPP-Architecture Generality.- 18.7 Bibliography.- 19 Distributed Intelligent Control Systems for an Unmanned Manufacturing Cell.- 19.1 Introduction.- 19.2 Control Algorithm.- 19.2.1 States of Control Algorithm.- 19.2.2 State of Robot.- 19.3 The Intelligent Control System.- 19.3.1 System Components.- 19.3.2 Production Rules.- 19.3.3 Simulation.- 19.4 Conclusion.- 19.5 Bibliography.- V Expert Systems Catalogues.- 20 A Survey of Expert System Tools and Engineering-Based Expert Systems.- 20.1 Introduction.- 20.2 An Overview of Expert System Development Tools.- 20.2.1 General Purpose Programming Languages.- 20.2.2 Expert System Shells.- 20.2.3 Expert System Languages.- 20.2.4 Multiple Paradigm Programming Environments.- 20.2.5 Skeletal Systems.- 20.2.6 Additional Modules.- 20.2.7 A Survey of Expert System Development Tools.- 20.3 A Survey on Engineering-Based Expert Systems.- 20.4 Conclusions.- 20.5 Bibliography.

O autorze

Expert system technology is receiving increasing popularity and acceptance in the engineering community. This book provides a balanced state-of-the-art presentation of te design principles of engineering expert systems, and a representative picture of their capabilities to assist efficiently the design, diagnosis and operation of complex industrial plants.