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Capturing Connectivity and Causality in Complex Industrial Processes

  • Fan Yang
  • Ping Duan
  • Sirish L. Shah
  • Tongwen Chen

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

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Fan Yang, Ping Duan, Sirish L. Shah, Tongwen Chen
    Pages 1-6
  3. Fan Yang, Ping Duan, Sirish L. Shah, Tongwen Chen
    Pages 7-11
  4. Fan Yang, Ping Duan, Sirish L. Shah, Tongwen Chen
    Pages 13-22
  5. Fan Yang, Ping Duan, Sirish L. Shah, Tongwen Chen
    Pages 23-39
  6. Fan Yang, Ping Duan, Sirish L. Shah, Tongwen Chen
    Pages 41-65
  7. Fan Yang, Ping Duan, Sirish L. Shah, Tongwen Chen
    Pages 67-89
  8. Back Matter
    Pages 91-91

About this book

Introduction

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.

Keywords

Causal Relationship Complex Systems Data Mining Fault Diagnosis Process Connectivity Process Knowledge System Topology

Authors and affiliations

  • Fan Yang
    • 1
  • Ping Duan
    • 2
  • Sirish L. Shah
    • 3
  • Tongwen Chen
    • 4
  1. 1.Department of Automation,Tsinghua Laboratory for Information Science and Technology,Tsinghua UniversityBeijingChina
  2. 2.Department of Electrical and Computer EngineeringUniversity of AlbertaEdmontonCanada
  3. 3.Department of Chemical and Materials EngineeringUniversity of AlbertaEdmontonCanada
  4. 4.Department of Electrical & Computer EngineeringUniversity of AlbertaEdmontonCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-05380-6
  • Copyright Information The Author(s) 2014
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-05379-0
  • Online ISBN 978-3-319-05380-6
  • Series Print ISSN 2191-530X
  • Series Online ISSN 2191-5318
  • Buy this book on publisher's site