Predictive maintenance of ATM machines by modelling remaining useful life with machine learning techniques
R. Rosati , L. Romeo, V. Vargas, P. Gutiérrez, C. Hervás-Martínez, L. Bianchini, A. Capriotti, R. Capparuccia, E. Frontoni
International workshop on soft computing models in industrial and environmental applications, pp. 239-249, 2022Abstract
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.
Cite this publication
BibTex
@inproceedings{rosati2022predictive,
author = {Riccardo Rosati and Luca Romeo and Víctor Manuel Vargas and Pedro Antonio Gutiérrez and César Hervás-Martínez and Lorenzo Bianchini and Alessandra Capriotti and Rosario Capparuccia and Emanuele Frontoni},
title = {Predictive maintenance of ATM machines by modelling remaining useful life with machine learning techniques},
booktitle = {International workshop on soft computing models in industrial and environmental applications},
year = {2022},
pages = {239--249},
doi = {10.1007/978-3-031-18050-7_23}
}
APA
Rosati, R., Romeo, L., Vargas, V., Gutiérrez, P., 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. In International workshop on soft computing models in industrial and environmental applications (pp. 239-249).
CV
R. Rosati (CA), L. Romeo, V.M. Vargas, 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". International workshop on soft computing models in industrial and environmental applications, pp. 239-249, 2022.
RIS
TY - CONF T1 - Predictive maintenance of ATM machines by modelling remaining useful life with machine learning techniques T2 - International workshop on soft computing models in industrial and environmental applications AU - Rosati, Riccardo AU - Romeo, Luca AU - Vargas, Víctor Manuel AU - Gutiérrez, Pedro Antonio AU - Hervás-Martínez, César AU - Bianchini, Lorenzo AU - Capriotti, Alessandra AU - Capparuccia, Rosario AU - Frontoni, Emanuele JO - International workshop on soft computing models in industrial and environmental applications JA - International workshop on soft computing models in industrial and environmental applications Y1 - 2022 PY - 2022 SP - 239 EP - 249 DO - 10.1007/978-3-031-18050-7_23 ER -