Ebook: Data Mining and Knowledge Discovery for Process Monitoring and Control
Author: Xue Z. Wang BEng MSc PhD (auth.)
- Tags: Database Management, Control, Information Storage and Retrieval, Industrial Chemistry/Chemical Engineering
- Series: Advances in Industrial Control
- Year: 1999
- Publisher: Springer-Verlag London
- Edition: 1
- Language: English
- pdf
Modern computer-based control systems are able to collect a large amount of information, display it to operators and store it in databases but the interpretation of the data and the subsequent decision making relies mainly on operators with little computer support. This book introduces developments in automatic analysis and interpretation of process-operational data both in real-time and over the operational history, and describes new concepts and methodologies for developing intelligent, state-space-based systems for process monitoring, control and diagnosis.
The book brings together new methods and algorithms from process monitoring and control, data mining and knowledge discovery, artificial intelligence, pattern recognition, and causal relationship discovery, as well as signal processing. It also provides a framework for integrating plant operators and supervisors into the design of process monitoring and control systems.
The topics covered include
• a fresh look at current systems for process monitoring, control and diagnosis
• a framework for developing intelligent, state-space-based systems
• a review of data mining and knowledge discovery
• data preprocessing for feature extraction, dimension reduction, noise removal and concept formation
• multivariate statistical analysis for process monitoring and control
• supervised and unsupervised methods for operational state identification
• variable causal relationship discovery in graphical models and production rules
• software sensor design
• historical data analysis
Data Mining and Knowledge Discovery for Process Monitoring and Control is important reading for researchers and graduate students in process control and data and knowledge engineering. Control and process engineers should also find this book of value.
Modern computer-based control systems are able to collect a large amount of information, display it to operators and store it in databases but the interpretation of the data and the subsequent decision making relies mainly on operators with little computer support. This book introduces developments in automatic analysis and interpretation of process-operational data both in real-time and over the operational history, and describes new concepts and methodologies for developing intelligent, state-space-based systems for process monitoring, control and diagnosis.
The book brings together new methods and algorithms from process monitoring and control, data mining and knowledge discovery, artificial intelligence, pattern recognition, and causal relationship discovery, as well as signal processing. It also provides a framework for integrating plant operators and supervisors into the design of process monitoring and control systems.
The topics covered include
• a fresh look at current systems for process monitoring, control and diagnosis
• a framework for developing intelligent, state-space-based systems
• a review of data mining and knowledge discovery
• data preprocessing for feature extraction, dimension reduction, noise removal and concept formation
• multivariate statistical analysis for process monitoring and control
• supervised and unsupervised methods for operational state identification
• variable causal relationship discovery in graphical models and production rules
• software sensor design
• historical data analysis
Data Mining and Knowledge Discovery for Process Monitoring and Control is important reading for researchers and graduate students in process control and data and knowledge engineering. Control and process engineers should also find this book of value.
Modern computer-based control systems are able to collect a large amount of information, display it to operators and store it in databases but the interpretation of the data and the subsequent decision making relies mainly on operators with little computer support. This book introduces developments in automatic analysis and interpretation of process-operational data both in real-time and over the operational history, and describes new concepts and methodologies for developing intelligent, state-space-based systems for process monitoring, control and diagnosis.
The book brings together new methods and algorithms from process monitoring and control, data mining and knowledge discovery, artificial intelligence, pattern recognition, and causal relationship discovery, as well as signal processing. It also provides a framework for integrating plant operators and supervisors into the design of process monitoring and control systems.
The topics covered include
• a fresh look at current systems for process monitoring, control and diagnosis
• a framework for developing intelligent, state-space-based systems
• a review of data mining and knowledge discovery
• data preprocessing for feature extraction, dimension reduction, noise removal and concept formation
• multivariate statistical analysis for process monitoring and control
• supervised and unsupervised methods for operational state identification
• variable causal relationship discovery in graphical models and production rules
• software sensor design
• historical data analysis
Data Mining and Knowledge Discovery for Process Monitoring and Control is important reading for researchers and graduate students in process control and data and knowledge engineering. Control and process engineers should also find this book of value.
Content:
Front Matter....Pages I-XVIII
Introduction....Pages 1-12
Data Mining and Knowledge Discovery — an Overview....Pages 13-28
Data Pre-Processing for Feature Extraction, Dimension Reduction and Concept Formation....Pages 29-60
Multivariate Statistical Analysis for Data Analysis and Statistical Control....Pages 61-84
Supervised Learning for Operational Support....Pages 85-117
Unsupervised Learning for Operational State Identification....Pages 119-147
Inductive Learning for Conceptual Clustering and Real-Time Process Monitoring....Pages 149-172
Automatic Extraction of Knowledge Rules from Process Operational Data....Pages 173-191
Inferential Models and Software Sensors....Pages 193-213
Concluding Remarks....Pages 215-216
Back Matter....Pages 217-251
Modern computer-based control systems are able to collect a large amount of information, display it to operators and store it in databases but the interpretation of the data and the subsequent decision making relies mainly on operators with little computer support. This book introduces developments in automatic analysis and interpretation of process-operational data both in real-time and over the operational history, and describes new concepts and methodologies for developing intelligent, state-space-based systems for process monitoring, control and diagnosis.
The book brings together new methods and algorithms from process monitoring and control, data mining and knowledge discovery, artificial intelligence, pattern recognition, and causal relationship discovery, as well as signal processing. It also provides a framework for integrating plant operators and supervisors into the design of process monitoring and control systems.
The topics covered include
• a fresh look at current systems for process monitoring, control and diagnosis
• a framework for developing intelligent, state-space-based systems
• a review of data mining and knowledge discovery
• data preprocessing for feature extraction, dimension reduction, noise removal and concept formation
• multivariate statistical analysis for process monitoring and control
• supervised and unsupervised methods for operational state identification
• variable causal relationship discovery in graphical models and production rules
• software sensor design
• historical data analysis
Data Mining and Knowledge Discovery for Process Monitoring and Control is important reading for researchers and graduate students in process control and data and knowledge engineering. Control and process engineers should also find this book of value.
Content:
Front Matter....Pages I-XVIII
Introduction....Pages 1-12
Data Mining and Knowledge Discovery — an Overview....Pages 13-28
Data Pre-Processing for Feature Extraction, Dimension Reduction and Concept Formation....Pages 29-60
Multivariate Statistical Analysis for Data Analysis and Statistical Control....Pages 61-84
Supervised Learning for Operational Support....Pages 85-117
Unsupervised Learning for Operational State Identification....Pages 119-147
Inductive Learning for Conceptual Clustering and Real-Time Process Monitoring....Pages 149-172
Automatic Extraction of Knowledge Rules from Process Operational Data....Pages 173-191
Inferential Models and Software Sensors....Pages 193-213
Concluding Remarks....Pages 215-216
Back Matter....Pages 217-251
....