Development process to bearing fault diagnostic and prognostic for the predictive maintenance era

Abstract

Today, the manufacturing industry seeks to improve competitiveness by converging on new technologies to ensure a new engine of growth, moreover, systems based on IoT and artificial intelligence are increasingly used in this convergence. This new industry must meet the challenges of productivity and competitiveness to interconnect the physical and digital world in which machines, information systems, and products communicate permanently, all to reduce consumers and maintain productivity gains and optimize them in terms of energy consumed reduced breakdowns... This article presents an original and innovative contribution. A new model has been proposed that summarizes an approach based on machine learning, intending to perform predictive maintenance based on artificial neural networks, considering the values acquired by sensors in real-time, it allows us a fast and very low implementation of predictive maintenance, particularly important for companies. The model is validated in real situations. The results show a very high level of accuracy.