Scalability Analysis of Predictive Maintenance Using Machine Learning in Oil Refineries

Abstract

Modern refineries typically use a high number of sensors that generate an enormous amount of data about the condition of the plants. This generated data can be used to perform predictive maintenance, an approach to predict impending failures and mitigate downtime in refineries. This research analyzes the scalability of machine learning methods for predictive maintenance solution in an oil refinery. It can be done by modeling the normal behavior of the plant and use the prediction error to identify anomalies which might potentially become failures. Several methods and learning algorithms are explored in this research to model the normal behavior of multiple components in the plant. The experiments are performed by using historical process data from a crude distiller unit at Shell Pernis Refinery. The results show that the proposed approach using multiple targets model is able to predict multiple components in the plant. It is not only able to detect anomalies but also identify the faulty component. Furthermore, it reduces the required time to model the normal behavior of the plant which improves the scalability of the predictive maintenance approach in the refinery.Computer Scienc