Predictive Maintenance of ATM machines by modelling Remaining Useful Life with Machine Learning techniques

Áreas de investigación:

• Sin categoría

Año:

2022

Tipo de publicación:

Artículo en conferencia

Autores:

• Rosati, Riccardo
• Romeo, Luca
• Vargas, Víctor Manuel
• Gutiérrez, Pedro Antonio
• Hervás-Martínez, César
• Bianchini, Lorenzo
• Capriotti, Alessandra
• Capparuccia, Rosario
• Frontoni, Emanuele

Volumen:

531

Título del libro:

Proceedings of the 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022)

Serie:

Lecture Notes in Networks and Systems

Páginas:

239-249

Organización:

Salamanca, Spain

Mes:

September

ISBN:

978-3-031-18049-1

BibTex:

@conference{VictorVargasATM-2022, author = "Riccardo Rosati and Luca Romeo and V{\'i}ctor Manuel Vargas and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Lorenzo Bianchini and Alessandra Capriotti and Rosario Capparuccia and Emanuele Frontoni", 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.", 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", isbn = "978-3-031-18049-1", month = "September", organization = "Salamanca, Spain", pages = "239--249", series = "Lecture Notes in Networks and Systems", title = "{P}redictive {M}aintenance of {ATM} machines by modelling {R}emaining {U}seful {L}ife with {M}achine {L}earning techniques", url = "doi.org/10.1007/978-3-031-18050-7_23", volume = "531", year = "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.