TY  - JOUR
ID  - alberto2024eaai
T1  - MachNet, a general Deep Learning architecture for Predictive Maintenance within the industry 4.0 paradigm
A1  - Jaenal, Alberto
A1  - Ruiz-Sarmiento, J. R.
A1  - Gonzalez-Jimenez, Javier
JA  - Engineering Applications of Artificial Intelligence
Y1  - 2024
VL  - 127
SP  - 107365
SN  - 0952-1976
UR  - https://www.sciencedirect.com/science/article/pii/S095219762301549X
M2  - doi: https://doi.org/10.1016/j.engappai.2023.107365
KW  - Artificial Intelligence
KW  - Deep Learning
KW  - Industry 4.0
KW  - Intelligent prognostics tools
KW  - Machine Learning
KW  - Predictive Maintenance
KW  - Smart manufacturing
N2  - 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.
M1  - img_url=
M1  - rank_indexname=JCR
M1  - rank_pos_in_category=5
M1  - rank_num_in_category=179
M1  - rank_cat_name=ENGINEERING%2C%20MULTIDISCIPLINARY
M1  - impact_factor=7.5
ER  -