Predictive maintenance in the Industry 4.0: A systematic literature review

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 futu...

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Veröffentlicht in:Computers & industrial engineering 2020-12, Vol.150, p.106889, Article 106889
Hauptverfasser: Zonta, Tiago, da Costa, Cristiano André, da Rosa Righi, Rodrigo, de Lima, Miromar José, da Trindade, Eduardo Silveira, Li, Guann Pyng
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Sprache:eng
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Zusammenfassung: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. •We review the state-of-the-art of predictive maintenance in the Industry 4.0.•The methodology consisted of a systematic literature review of the area.•We consider time-based approaches as the main challenge for predictive maintenance.•We present a taxonomy for monitoring in the context of the Industry 4.0.•We highlight the multidisciplinarity involved and the need for integration.
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2020.106889