Predictive Maintenance of ATM machines by modelling Remaining Useful Life with Machine Learning techniques
Riccardo Rosati 
Luca Romeo 
Víctor Manuel Vargas-Yun 
Pedro Antonio Gutiérrez 
César Hervás-Martínez Proceedings of the 17th international conference on soft computing models in industrial and environmental applications (SOCO 2022)
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.
BibTex Citation
@inproceedings{Rosati2022Predictive,
author = {Rosati, Riccardo and Romeo, Luca and Vargas-Yun, V{\' i}ctor Manuel and Guti{\' e}rrez, Pedro Antonio and Herv{\' a}s-Mart{\' i}nez, C{\' e}sar and Bianchini, Lorenzo and Capriotti, Alessandra and Capparuccia, Rosario and Frontoni, Emanuele},
booktitle = {Proceedings of the 17th international conference on soft computing models in industrial and environmental applications ({SOCO} 2022)},
doi = {10.1007/978-3-031-18050-7_23},
year = {2022},
pages = {239--249},
title = {Predictive {Maintenance} of {ATM} machines by modelling {Remaining} {Useful} {Life} with {Machine} {Learning} techniques},
url = {https://doi.org/10.1007/978-3-031-18050-7_23},
howpublished = {https://doi.org/10.1007/978-3-031-18050-7\textunderscore{}23},
volume = {531},
}
BibTex Unicode Citation
@inproceedings{Rosati2022Predictive,
author = {Rosati, Riccardo and Romeo, Luca and Vargas-Yun, Víctor Manuel and Gutiérrez, Pedro Antonio and Hervás-Martínez, César and Bianchini, Lorenzo and Capriotti, Alessandra and Capparuccia, Rosario and Frontoni, Emanuele},
booktitle = {Proceedings of the 17th international conference on soft computing models in industrial and environmental applications ({SOCO} 2022)},
doi = {10.1007/978-3-031-18050-7_23},
year = {2022},
pages = {239--249},
title = {Predictive {Maintenance} of {ATM} machines by modelling {Remaining} {Useful} {Life} with {Machine} {Learning} techniques},
url = {https://doi.org/10.1007/978-3-031-18050-7_23},
howpublished = {https://doi.org/10.1007/978-3-031-18050-7\textunderscore{}23},
volume = {531},
}
APA Citation
Rosati, R., Romeo, L., Vargas-Yun, V. M., Gutiérrez, P. A., Hervás-Martínez, C., Bianchini, L., Capriotti, A., Capparuccia, R., & Frontoni, E. (2022). Predictive Maintenance of ATM machines by modelling Remaining Useful Life with Machine Learning techniques. Proceedings of the 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022), 531, 239–249. https://doi.org/10.1007/978-3-031-18050-7_23
RIS Citation
TY - CONF
AU - Rosati, Riccardo
AU - Romeo, Luca
AU - Vargas-Yun, Víctor Manuel
AU - Gutiérrez, Pedro Antonio
AU - Hervás-Martínez, César
AU - Bianchini, Lorenzo
AU - Capriotti, Alessandra
AU - Capparuccia, Rosario
AU - Frontoni, Emanuele
C3 - Proceedings of the 17th international conference on soft computing mod
els in industrial and environmental applications (SOCO 2022)
DA - 2022///
C2 - 2022
DO - 10.1007/978-3-031-18050-7_23
ID - temp_id_092087132242
SP - 239-249
TI - Predictive Maintenance of ATM machines by modelling Remaining Useful L
ife with Machine Learning techniques
UR - https://doi.org/10.1007/978-3-031-18050-7_23
VL - 531
ER -
CV Citation
R. Rosati (CA), L. Romeo, V.M. Vargas-Yun, P.A. Gutiérrez, C. Hervás-Martínez, L. Bianchini, A. Capriotti, R. Capparuccia, E. Frontoni (3/9). "Predictive Maintenance of ATM machines by modelling Remaining Useful Life with Machine Learning techniques". Proceedings of the 17th international conference on soft computing models in industrial and environmental applications (SOCO 2022), pp. 239–249, 2022.