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Capturing Causality from Process Data

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Capturing Connectivity and Causality in Complex Industrial Processes

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

Abstract

Data is a valuable resource for modeling and analysis. Process data is a set of timeseries of process variables. In this chapter, we focus on the relationship between different time series to capture causality in the process. For a pair of process variables, various data-based methods can be applied to detect causality. These methods can be categorized into three classes: lag-basedmethods, such as the Granger causality and transfer entropy; conditional independence methods, such as the Bayesian network; and higher order statistics, such as the Patel’s pairwise conditional probability approach. In this work, we focus on the first group of methods, which are the most commonly used, and then briefly discuss some remaining methods. Based on the results of pairwise causality analysis, one can construct a causal network that is composed of the links between every two nodes. For multivariate systems, network topology can be determined by using statistical confounding analysis.

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Authors and Affiliations

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

    Fan Yang

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

    Ping Duan & Tongwen Chen

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

    Sirish L. Shah

Authors
  1. Fan Yang
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  2. Ping Duan
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  3. Sirish L. Shah
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  4. Tongwen Chen
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Corresponding author

Correspondence to Fan Yang .

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Yang, F., Duan, P., Shah, S.L., Chen, T. (2014). Capturing Causality from Process Data. In: Capturing Connectivity and Causality in Complex Industrial Processes. SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-05380-6_5

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  • DOI: https://doi.org/10.1007/978-3-319-05380-6_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05379-0

  • Online ISBN: 978-3-319-05380-6

  • eBook Packages: EngineeringEngineering (R0)

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