Predictive maintenance in the Industry 4.0: A systematic literature review. Zonta, T., da Costa, C. A., da Rosa Righi, R., de Lima, M. J., da Trindade, E. S., & Li, G. P. Computers & Industrial Engineering, 150:106889, December, 2020. ![Predictive maintenance in the Industry 4.0: A systematic literature review [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Industry 4.0 is collaborating directly for the technological revolution. Both machines and managers are daily confronted with decision making involving a massive input of data and customization in the manufacturing process. The ability to predict the need for maintenance of assets at a specific future moment is one of the main challenges in this scope. The possibility of performing predictive maintenance contributes to enhancing machine downtime, costs, control, and quality of production. We observed that surveys and tutorials about Industry 4.0 focus mainly on addressing data analytics and machine learning methods to change production procedures, so not comprising predictive maintenance methods and their organization. In this context, this article presents a systematic literature review of initiatives of predictive maintenance in Industry 4.0, identifying and cataloging methods, standards, and applications. As the main contributions, this survey discusses the current challenges and limitations in predictive maintenance, in addition to proposing a novel taxonomy to classify this research area considering the needs of the Industry 4.0. We concluded that computer science, including artificial intelligence and distributed computing fields, is more and more present in an area where engineering was the dominant expertise, so detaching the importance of a multidisciplinary approach to address Industry 4.0 effectively.
@article{zonta_predictive_2020,
title = {Predictive maintenance in the {Industry} 4.0: {A} systematic literature review},
volume = {150},
issn = {0360-8352},
shorttitle = {Predictive maintenance in the {Industry} 4.0},
url = {http://www.sciencedirect.com/science/article/pii/S0360835220305787},
doi = {10.1016/j.cie.2020.106889},
abstract = {Industry 4.0 is collaborating directly for the technological revolution. Both machines and managers are daily confronted with decision making involving a massive input of data and customization in the manufacturing process. The ability to predict the need for maintenance of assets at a specific future moment is one of the main challenges in this scope. The possibility of performing predictive maintenance contributes to enhancing machine downtime, costs, control, and quality of production. We observed that surveys and tutorials about Industry 4.0 focus mainly on addressing data analytics and machine learning methods to change production procedures, so not comprising predictive maintenance methods and their organization. In this context, this article presents a systematic literature review of initiatives of predictive maintenance in Industry 4.0, identifying and cataloging methods, standards, and applications. As the main contributions, this survey discusses the current challenges and limitations in predictive maintenance, in addition to proposing a novel taxonomy to classify this research area considering the needs of the Industry 4.0. We concluded that computer science, including artificial intelligence and distributed computing fields, is more and more present in an area where engineering was the dominant expertise, so detaching the importance of a multidisciplinary approach to address Industry 4.0 effectively.},
language = {en},
urldate = {2020-10-12},
journal = {Computers \& Industrial Engineering},
author = {Zonta, Tiago and da Costa, Cristiano André and da Rosa Righi, Rodrigo and de Lima, Miromar José and da Trindade, Eduardo Silveira and Li, Guann Pyng},
month = dec,
year = {2020},
keywords = {Artificial intelligence, Conditional-based maintenance, Industry 4.0, Predictive Maintenance, Remaining Useful Life},
pages = {106889},
}
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