Predictive Analytics in a Pulp Mill using Factory Automation Dataxe2x80x94Hidden Potential 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.

Unsupervised Online Anomaly Detection on Multivariate Sensing Time Series Data for Smart Manufacturing The emergence of IoT and AI has brought revolutionary change in various application domains. One of them is Industry 4.0, also called Smart Manufacturing, which aims to achieve highly flexible and automated production processes. In this paper, we study a use case of anomaly detection in smart manufacturing using the real data collected from the sensing devices of a factory production line. Our goal is to improve the anomaly detection accuracy at an earlier stage of production line, so that cost and time wasted by possible production failures can be reduced. To overcome the limited and irregular anomaly patterns found from our multivariate sensor dataset, we proposed an unsupervised realtime anomaly detection algorithm based on LSTMbased AutoEncoder. Our evaluations show that our approach achieved almost 90% accuracy for both precision and recall while other classification or regression based methods only reached 70%85%.