SiMoDiM: Monitoring and Diagnostics for the Digital Transformation of Predictive and Corrective Maintenance of Industrial Machinery in the Hot Rolling Mill Process

(Mar’17–Jun’20)
Tech. Transfer Project

@article{alberto2024eaai,

title = {MachNet, a general Deep Learning architecture for Predictive Maintenance within the industry 4.0 paradigm},

author = {Jaenal, Alberto and Ruiz-Sarmiento, J. R. and Gonzalez-Jimenez, Javier},

url = {https://www.sciencedirect.com/science/article/pii/S095219762301549X},

doi = {10.1016/j.engappai.2023.107365},

issn = {0952-1976},

year  = {2024},

date = {2024-01-01},

journal = {Engineering Applications of Artificial Intelligence},

volume = {127},

pages = {107365},

abstract = {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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jotaraul_kbs_2020,

title = {A Predictive Model for the Maintenance of Industrial Machinery in the Context of Industry 4.0},

author = {Ruiz-Sarmiento, J. R. and Monroy, Javier and Moreno, Francisco-Angel and Galindo, Cipriano and Bonelo, J. M. and Gonzalez-Jimenez, Javier},

url = {https://mapir.isa.uma.es/papersrepo/2020/2020-raul-A_Predictive_Model_for_the_Maintenance_of_Industrial_Machinery_in_the_Context_of_Industry_4.0.pdf},

doi = {10.1016/j.engappai.2019.103289},

issn = {0952-1976},

year  = {2020},

date = {2020-01-01},

journal = {Engineering Applications of Artificial Intelligence},

volume = {87},

keywords = {},

pubstate = {published},

tppubtype = {article}

}