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.