Prediction of Sensor Values in Paper Pulp Industry Using Neural Networks

Abstract

The economic sustainability of any industry is directly linked to the management and efficiency of its physical assets. The maintenance of these assets is one of the key elements for the success of a company since it represents a relevant part of its Capital and Operational Expenses (CAPEX and OPEX). Due to the importance of maintenance, a lot of research has been done to improve the methodologies aiming to maximize physical assets’ availability at the most rational costs. The introduction of Artificial Intelligence in the world of maintenance increased the quality of prediction on equipment failures, namely when associated to continuous equipment monitoring. This paper presents a case study where a neural network is proposed to predict the future values of various sensors installed on a paper pulp press. Data from the following variables is processed: electric current; pressure; temperature; torque; and speed.