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An approach to data structuring and predictive analysis in discrete manufacturing

Abstract

In discrete manufacturing the variation in process parameters and duration is often large. Common data storage and analytics systems primarily store data in univariate time series, and when analysing machine components of strongly varying lifetime and behaviour this causes a challenge. This paper presents a data structure and an analysis method for outlier detection which intends to deal with this challenge, as an alternative to predictive maintenance which often requires more data with higher quality than what is available. A case study in aluminium extrusion billet manufacturing is used to demonstrate the approach, predominantly detecting anomalies at the end of a critical component’s lifetime.
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Category

Academic article

Language

English

Author(s)

Affiliation

  • SINTEF Manufacturing

Year

2021

Published in

Procedia CIRP

Volume

104

Page(s)

1334 - 1338

View this publication at Norwegian Research Information Repository