Overview
- 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)
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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.
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Table of contents (6 chapters)
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Bibliographic Information
Book Title: Capturing Connectivity and Causality in Complex Industrial Processes
Authors: Fan Yang, Ping Duan, Sirish L. Shah, Tongwen Chen
Series Title: SpringerBriefs in Applied Sciences and Technology
DOI: https://doi.org/10.1007/978-3-319-05380-6
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2014
Softcover ISBN: 978-3-319-05379-0Published: 10 April 2014
eBook ISBN: 978-3-319-05380-6Published: 01 April 2014
Series ISSN: 2191-530X
Series E-ISSN: 2191-5318
Edition Number: 1
Number of Pages: XIII, 91
Number of Illustrations: 30 b/w illustrations, 24 illustrations in colour
Topics: Complexity, Mathematical Modeling and Industrial Mathematics, Control and Systems Theory, Industrial Chemistry/Chemical Engineering, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences