RT Journal Article
T1 A predictive model for the maintenance of industrial machinery in the context of industry 4.0
A1 Ruiz-Sarmiento, José Raúl
A1 González-Monroy, Javier
A1 Moreno-Dueñas, Francisco Ángel
A1 Galindo-Andrades, Cipriano
A1 Bonelo, Jose-Maria
A1 González-Jiménez, Antonio Javier
K1 Maquinaria - Mantenimiento y reparación - Proceso de datos
AB The Industry 4.0 paradigm is being increasingly adopted in the production, distribution and commercialization chains worldwide. The integration of the cutting-edge techniques behind it entails a deep and complex revolution –changing from scheduled-based processes to smart, reactive ones– that has to be thoroughly applied at different levels. Aiming to shed some light on the path towards such evolution, this work presents an Industry 4.0 based approach for facing a key aspect within factories: the health assessment of critical assets. This work is framed in the context of the innovative project SiMoDiM, which pursues the design and integration of a predictive maintenance system for the stainless steel industry. As a case of study, it focuses on the machinery involved in the production of high-quality steel sheets, i.e. the Hot Rolling Process, and concretely on predicting the degradation of the drums within the heating coilers of Steckel mills (parts with an expensive replacement that work under severe mechanical and thermal stresses). This paper describes a predictive model based on a Bayesian Filter, a tool from the Machine Learning field, to estimate and predict the gradual degradation of such machinery, permitting the operators to make informed decisions regarding maintenance operations. For achieving that, the proposed model iteratively fuses expert knowledge with real time information coming from the hot rolling processes carried out in the factory. The predictive model has been fitted and evaluated with real data from ∼118k processes, proving its virtues for promoting the Industry 4.0 era.
PB Elsevier
YR 2020
FD 2020-01
LK https://hdl.handle.net/10630/29794
UL https://hdl.handle.net/10630/29794
LA eng
NO Jose-Raul Ruiz-Sarmiento, Javier Monroy, Francisco-Angel Moreno, Cipriano Galindo, Jose-Maria Bonelo, Javier Gonzalez-Jimenez, A predictive model for the maintenance of industrial machinery in the context of industry 4.0, Engineering Applications of Artificial Intelligence, Volume 87, 2020, 103289, ISSN 0952-1976.
NO Ministerio de Industria, Energía y Turismo (OTRI-8.06/5.56.4826, IC4-030000-2016-3), MInisterio de Economía, Industria y Competitividad (DPI2014-55826-R). Universidad de Málaga (I-PPIT-UMA)
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026