RT Journal Article
T1 MachNet, a general deep learning architecture for predictive maintenance within the industry 4.0 paradigm
A1 Jaenal, Alberto
A1 Ruiz-Sarmiento, José Raúl
A1 González-Jiménez, Antonio Javier
K1 Ingeniería de sistemas
AB In the Industry 4.0 era, a myriad of sensors of diverse nature (temperature, pressure, etc.) is spreading throughout the entire value chain of industries, being potentially exploitable for multiple purposes, such as Predictive Maintenance (PdM): the just-in-time maintenance of industrial assets, which results in reduced operating costs, increased operator safety, etc. Nowadays, industrial processes require to be highly configurable, in order to proactively adapt their operation to diverse factors such as user needs, product updates or supply chain uncertainties. This limits current Industry 4.0-PdM solutions, typically consisting of ad-hoc developments intended for specific scenarios, i.e. they are designed to operate under certain conditions (configurations, employed sensors, etc.), being unable to manage changes in their setup.This paper presents a general Deep Learning (DL) architecture, MachNet, which deals with such heterogeneity and is able to address PdM problems of a diverse nature. The modularity of the proposed architecture enables it to deal with an arbitrary number of sensors of different types, also allowing the integration of prior information (age of assets, material type, etc.), which clearly affects performance and is often neglected. In practice, our architecture effortlessly adapts to the assets’ specifications and to different PdM problems. That is, MachNet becomes an architectural template that can be instantiated for a given scenario. We tested our proposal in two different PdM-related problems: Health State (HS) and Remaining-useful-Life (RuL) estimation, achieving in both cases comparable or superior performance to other state-of-the-art approaches, with the additional advantage of the generality that MachNet offers.
PB Elsevier
YR 2023
FD 2023-11-02
LK https://hdl.handle.net/10630/28803
UL https://hdl.handle.net/10630/28803
LA eng
NO Alberto Jaenal, Jose-Raul Ruiz-Sarmiento, Javier Gonzalez-Jimenez, MachNet, a general Deep Learning architecture for Predictive Maintenance within the industry 4.0 paradigm, Engineering Applications of Artificial Intelligence, Volume 127, Part B, 2024, 107365, ISSN 0952-1976, https://doi.org/10.1016/j.engappai.2023.107365
NO Funding for open Access charge: Universidad de Málaga / CBUA.Work partially supported by the grant program FPU17/04512 and the research project ARPEGGIO ([PID2020-117057GB-I00]), both funded by the Spanish Government, and the research project HOUNDBOT ([P20-01302]), financed by the Regional Government of Andalusia with support from the ERDF (European Regional Development Funds). The authors thank the Supercomputing and Bioinnovation Center (SCBI) of the University of Málaga for their provision of computational resources and technical support (www.scbi.uma.es/site); and the support of NVIDIA Corporation with the donation of the Titan X Pascal used for this research.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026