Predictive Analytics in a Pulp Mill using Factory Automation Data—Hidden Potential

Abstract

Industrial automation systems have collected vast amounts of data for years. Data analytics and machine learning can be used to reveal different phenomena and anomalies, which may be otherwise impossible to see. However, the opportunities offered by the data are not currently utilized even though the technology is available. In this paper, a the potential use of the data analytics and machine learning of automation system data is presented. A case study on indirect measurement and predictive analysis of electric motor overcurrent was carried out in a pulp mill. Predictive models reached accuracy up to 98,85 %. The methods presented can be generalized to other processes. Since automation systems store data in most industrial sites, no additional hardware is necessarily needed for industrial internet of things (IIoT) systems, making a factory scale IIoT system possible.