Skip to main content
SpringerLink
Log in

Data Mining and Knowledge Discovery for Process Monitoring and Control

  • Book
  • © 1999

Overview

Authors:
  1. Xue Z. Wang

    1. Department of Chemical Engineering, University of Leeds, Leeds, UK

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • Features the subjects of on-line signal preprocessing, feature extraction and concept formation, operational state identification and automatic generation of decision trees and production rules from data.
  • This is the first book to address the application of data mining to process monitoring and control.
  • Wide ranging readership covering theoreticians and practitioners

Part of the book series: Advances in Industrial Control (AIC)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (10 chapters)

  1. Front Matter

    Pages I-XVIII
    Download chapter PDF

  2. Introduction

    • Xue Z. Wang
    Pages 1-12

  3. Data Mining and Knowledge Discovery — an Overview

    • Xue Z. Wang
    Pages 13-28

  4. Data Pre-Processing for Feature Extraction, Dimension Reduction and Concept Formation

    • Xue Z. Wang
    Pages 29-60

  5. Multivariate Statistical Analysis for Data Analysis and Statistical Control

    • Xue Z. Wang
    Pages 61-84

  6. Supervised Learning for Operational Support

    • Xue Z. Wang
    Pages 85-117

  7. Unsupervised Learning for Operational State Identification

    • Xue Z. Wang
    Pages 119-147

  8. Inductive Learning for Conceptual Clustering and Real-Time Process Monitoring

    • Xue Z. Wang
    Pages 149-172

  9. Automatic Extraction of Knowledge Rules from Process Operational Data

    • Xue Z. Wang
    Pages 173-191

  10. Inferential Models and Software Sensors

    • Xue Z. Wang
    Pages 193-213

  11. Concluding Remarks

    • Xue Z. Wang
    Pages 215-216

  12. Back Matter

    Pages 217-251
    Download chapter PDF
Back to top

Keywords

About this book

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.

Authors and Affiliations

  • Department of Chemical Engineering, University of Leeds, Leeds, UK

    Xue Z. Wang

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation