ENBIS-18 in Nancy

2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018

Big Data Strategies for Online Monitoring of Processes

5 September 2018, 11:10 – 11:30

Abstract

Submitted by
Flavia Dalia Frumosu
Authors
Flavia Dalia Frumosu (Technical University of Denmark), Murat Kulahci (Technical University of Denmark)
Abstract
More and more high frequency and high dimensional data is becoming available particularly in production processes. In manufacturing, this phenomenon is often a direct result of digitalization of production systems as in Industry 4.0. Latent structures based methods are often employed in the analysis of multivariate and complex data. In processes with fast production rate, data on the quality characteristics of the process output tends to be scarcer than the available process data, which is generated through multiple sensors and automated data collection schemes. The research question addressed in this work is how to use all available process data in the pursuit of better process monitoring and control by means of new strategies and latent structure based methods. More precisely, a well-defined strategy for data collection is expected to improve the prediction of quality characteristics and ultimately the performance of online monitoring processes. In this work, we will discuss our proposed approach in the pursuit of such strategy and provide some examples on its execution.
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