Data Mining and Knowledge Discovery for Process Monitoring and Control (Advances in Industrial Control)
| Author | : | |
| Rating | : | 4.28 (668 Votes) |
| Asin | : | 1852331372 |
| Format Type | : | paperback |
| Number of Pages | : | 251 Pages |
| Publish Date | : | 2016-07-13 |
| Language | : | English |
DESCRIPTION:
A good book in term of theory, a collage for practitioners! A Customer Enter,at last, Data Mining in the field of process control. This book by Prof.Wang although considers data mining- its utility and issues at length, stops short of giving insight into the implementation details. No doubt, the author has cited instances to put through the basic ideas, but i
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 analysisData 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. T
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. 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. 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.