Data Modeling and Knowledge Discovery in Process Industries

Data Modeling and Knowledge Discovery in Process Industries

Benjamin Klöpper, Marcel Dix, David Arnu, Dikshith Siddapura
Copyright: © 2016 |Pages: 11
DOI: 10.4018/978-1-5225-0293-7.ch009
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Abstract

Dispersed data sources, incompatible data formats and a lack of non-ambiguous and machine readable meta-data is a major obstacle in data analytics and data mining projects in process industries. Often, meta-information is only available in unstructured format optimized for human consumption. This contribution outlines a feasible methodology for organizing historical datasets extracted from process plants in a big data platform for the purpose of analytics and machine learning model building in an industrial big data analytics project.
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A Typical Data Analytics Project In Process Industries

From a business model perspective, such a prediction feature for industrial control systems could be delivered to plant operators as a product (e.g. as a control system product extension), as a service (e.g. a data analytics consultation service for existing plants), or a combination of both (as so-called product-service-system or PSS, cf. Mont, 2001). In either way, the underlying development project to create prediction models in process industries will deal with similar tasks which are illustrated in Figure 1 and explained in the following. As we will see in this chapter, a key challenge in industrial data analytics is: understanding the customer problem and data, in order to prepare this data for the prediction model development.

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