Skip to main content
SpringerLink
Log in
Book cover

Capturing Connectivity and Causality in Complex Industrial Processes

  • Book
  • © 2014

Overview

Authors:
  1. Fan Yang

    1. Department of Automation,Tsinghua Laboratory for Information Science and Technology,, Tsinghua University, Beijing, China

    View author publications

    You can also search for this author in PubMed   Google Scholar

  2. Ping Duan

    1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada

    View author publications

    You can also search for this author in PubMed   Google Scholar

  3. Sirish L. Shah

    1. Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Canada

    View author publications

    You can also search for this author in PubMed   Google Scholar

  4. Tongwen Chen

    1. Department of Electrical & Computer Engineering, University of Alberta, Edmonton, Canada

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • Provides an exhaustive overview of concepts and descriptions of connectivity and causality in complex processes
  • Explains how to obtain an acceptable process topology from the fusion of different information resources
  • Tutorial style deepens understanding of classical and recent research results with existing and potential applications
  • Includes supplementary material: sn.pub/extras

Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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 (6 chapters)

  1. Front Matter

    Pages i-xiii
    Download chapter PDF

  2. Introduction

    • Fan Yang, Ping Duan, Sirish L. Shah, Tongwen Chen
    Pages 1-6

  3. Examples of Applications for Connectivity and Causality Analysis

    • Fan Yang, Ping Duan, Sirish L. Shah, Tongwen Chen
    Pages 7-11

  4. Description of Connectivity and Causality

    • Fan Yang, Ping Duan, Sirish L. Shah, Tongwen Chen
    Pages 13-22

  5. Capturing Connectivity and Causality from Process Knowledge

    • Fan Yang, Ping Duan, Sirish L. Shah, Tongwen Chen
    Pages 23-39

  6. Capturing Causality from Process Data

    • Fan Yang, Ping Duan, Sirish L. Shah, Tongwen Chen
    Pages 41-65

  7. Case Studies

    • Fan Yang, Ping Duan, Sirish L. Shah, Tongwen Chen
    Pages 67-89

  8. Back Matter

    Pages 91-91
    Download chapter PDF
Back to top

Keywords

About this book

This brief reviews concepts of inter-relationship in modern industrial processes, biological and social systems. Specifically ideas of connectivity and causality within and between elements of a complex system are treated; these ideas are of great importance in analysing and influencing mechanisms, structural properties and their dynamic behaviour, especially for fault diagnosis and hazard analysis. Fault detection and isolation for industrial processes being concerned with root causes and fault propagation, the brief shows that, process connectivity and causality information can be captured in two ways:

·      from process knowledge: structural modeling based on first-principles structural models can be merged with adjacency/reachability matrices or topology models obtained from process flow-sheets described in standard formats; and

·      from process data: cross-correlation analysis, Granger causality and its extensions, frequency domain methods, information-theoretical methods, and Bayesian networks can be used to identify pair-wise relationships and network topology.

These methods rely on the notion of information fusion whereby process operating data is combined with qualitative process knowledge, to give a holistic picture of the system.

Authors and Affiliations

  • Department of Automation,Tsinghua Laboratory for Information Science and Technology,, Tsinghua University, Beijing, China

    Fan Yang

  • Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada

    Ping Duan

  • Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Canada

    Sirish L. Shah

  • Department of Electrical & Computer Engineering, University of Alberta, Edmonton, Canada

    Tongwen Chen

About the authors

The authors jointly have extensive research experience in modeling, control, and monitoring of complex industrial processes. In particular, they have worked on industrial projects in oil and petrochemical sectors to address safety, alarm, and fault diagnosis issues from operating plants. Moreover, they have conducted research in the related areas on capturing connectivity and causality using process data and various forms of process knowledge; their research results have been published in international journals, benefiting the automation community. Realizing the importance of capturing connectivity and causality in real-world problems, and summarizing their knowledge and understanding on various approaches currently available, the authors have made a great effort in presenting this brief as an introduction, a survey, and also a tutorial on this seasoned topic.

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation