KnE Engineering

ISSN: 2518-6841

The latest conference proceedings on all fields of engineering.

Modeling System Based on Machine Learning Approaches for Predictive Maintenance Applications

Published date: Jun 02 2020

Journal Title: KnE Engineering

Issue title: International Congress on Engineering — Engineering for Evolution

Pages: 857–871

DOI: 10.18502/keg.v5i6.7105

Authors:

João Pedro Serrasqueiro Martinsjoao.serrasqueiro.mec@gmail.comADEM/ISEL/IPL, Instituto Superior de Engenharia de Lisboa

Filipe Martins RodriguesADEM/ISEL/IPL, Instituto Superior de Engenharia de Lisboa

Nuno Paulo Ferreira HenriquesADEM/ISEL/IPL, Instituto Superior de Engenharia de Lisboa

Abstract:

Industry 4.0 must respond to some challenges such as the flexibility and robustness of unexpected conditions, as well as the degree of system autonomy, something that is still lacking. The evolution of Industry 4.0 aims at converting purely mechanical machines into machines with self-learning capacity in order to improve overall performance  and contribute to the optimization of maintenance. An important contribution of Industry 4.0 in the industrial sector is predictive maintenance and prescriptive maintenance. This article should be analysed as a methodology proposal to implement an automatic forecasting model in a test bench for the recognition of a machine’s failure and contribute to the development of algorithms for preventive and descriptive maintenance.

Keywords: Industry 4.0, Artificial intelligence, Machine learning, Predictive maintenance, Prescriptive maintenance

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