Abstract
One of the main relevant topics of Industry 4.0 is related to the prediction of Remaining Useful Life (RUL) of machines. In this context, the “Smart Manufacturing Machine with Predictive Lifetime Electronic maintenance” (SIMPLE) project aims to promote collaborations among different companies in the scenario of predictive maintenance. One of the topics of the SIMPLE project is related to the prediction of RUL of automated teller machines (ATMs). This represents a key task as these machines are subject to different types of failure. However the main challenges in this field lie in: i) collecting a representative dataset, ii) correctly annotating the observations and iii) handling the imbalanced nature of the dataset. To overcome this problem, in this work we present a feature extraction strategy and a machine learning (ML) based solution for solving RUL estimation for ATM devices. We prove the effectiveness of our approach with respect to other state-of-the-art ML approaches widely employed for solving the RUL task. In addition, we propose the design of a predictive maintenance platform to integrate our ML model for the SIMPLE project.
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Acknowledgements
This work was supported within the research agreement between Università Politecnica delle Marche and Sigma Spa for the project “Smart Manufacturing Machine with Predictive Lifetime Electronic maintenance (SIMPLE)” funded by Ministero dello Sviluppo Economico (Italia) - Fondo per la Crescita Sostenibile - Accordi per l’innovazione di cui al D.M. 24 maggio 2017. This work has been partially subsidised by “Agencia Española de Investigación (España)” (grant reference: PID2020-115454GB-C22/AEI/10.13039/501100011033), by “Consejería de Salud y Familia (Junta de Andalucía)” (grant reference: PS-2020-780) and by “Consejería de Transformación Económica, Industria, Conocimiento y Universidades (Junta de Andalucía) y Programa Operativo FEDER 2014-2020” (grant references: UCO-1261651 and PY20_00074). Víctor Manuel Vargas’s research has been subsidised by the FPU Predoctoral Program of the Spanish Ministry of Science, Innovation and Universities (MCIU), grant reference FPU18/00358.
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Rosati, R. et al. (2023). Predictive Maintenance of ATM Machines by Modelling Remaining Useful Life with Machine Learning Techniques. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_23
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