REPLICA: A solution for next generation iot and digital twin based fault diagnosis and predictive maintenance

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

Rossini, R (Author) Other; Conzon, D (Author) Other; Prato, G (Author) Other; Pastrone, C (Author) Other; Reis, J (Author) Other; Gil Manuel Gonçalves (Author) FEUP

Conference proceedings International

Title: CEUR Workshop Proceedings

Pages: 55-62

1st Eclipse Research International Conference on Security, Artificial Intelligence and Modeling for the Next Generation Internet of Things, SAM IoT 2020

17 September 2020 through 18 September 2020

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-00T-2Z3

Abstract (EN): Nowadays competitiveness goes through several aspects: digitalization, productivity and environmental impact. Technology is advancing fast and helping industries to obtain more and more detailed data about their processes and equipment. In fact, the possibility to monitor and control each part of the process is a strong base on which a more intelligent and focused control can be built. Technology advance brings innovation and the possibility to manage the production in terms of¿near future¿ through AI prediction and decision-making support. Forecasting demands and planning production, optimizing process by reducing costs and improving efficiency without corrupting the quality of the product is a big challenge at the plant level. In this paper, a flexible, scalable architecture for intelligent digital twin realization called REPLICA has been proposed to cope with such problem and help industries to advance and discover possible optimizations. This architecture sits on top of two European projects, namely CPSwarm and RECLAIM, where their contribution focus on distributed simulation and optimization, and Adaptive Sensorial Networks, correspondingly. As a validation process, a hypothetical use case is presented, detailing the key differentiating points and benefits of the proposed architecture. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Language: English

Type (Professor's evaluation): Scientific