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Article

TIP4.0: Industrial Internet of Things Platform for Predictive Maintenance

1
Associação Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal
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Laboratório de Instrumentação, Engenharia Biomédica e Física da Radiação (LIBPhys-UNL), Departamento de Física, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
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Institute of Informatics (INF), Federal University of Goiás (UFG), Goiânia 74690-900, Brazil
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Bresimar Automação S.A., 3800-230 Aveiro, Portugal
*
Author to whom correspondence should be addressed.
Academic Editor: Reza Malekian
Sensors 2021, 21(14), 4676; https://doi.org/10.3390/s21144676
Received: 15 June 2021 / Revised: 2 July 2021 / Accepted: 4 July 2021 / Published: 8 July 2021
(This article belongs to the Section Internet of Things)
Industry 4.0, allied with the growth and democratization of Artificial Intelligence (AI) and the advent of IoT, is paving the way for the complete digitization and automation of industrial processes. Maintenance is one of these processes, where the introduction of a predictive approach, as opposed to the traditional techniques, is expected to considerably improve the industry maintenance strategies with gains such as reduced downtime, improved equipment effectiveness, lower maintenance costs, increased return on assets, risk mitigation, and, ultimately, profitable growth. With predictive maintenance, dedicated sensors monitor the critical points of assets. The sensor data then feed into machine learning algorithms that can infer the asset health status and inform operators and decision-makers. With this in mind, in this paper, we present TIP4.0, a platform for predictive maintenance based on a modular software solution for edge computing gateways. TIP4.0 is built around Yocto, which makes it readily available and compliant with Commercial Off-the-Shelf (COTS) or proprietary hardware. TIP4.0 was conceived with an industry mindset with communication interfaces that allow it to serve sensor networks in the shop floor and modular software architecture that allows it to be easily adjusted to new deployment scenarios. To showcase its potential, the TIP4.0 platform was validated over COTS hardware, and we considered a public data-set for the simulation of predictive maintenance scenarios. We used a Convolution Neural Network (CNN) architecture, which provided competitive performance over the state-of-the-art approaches, while being approximately four-times and two-times faster than the uncompressed model inference on the Central Processing Unit (CPU) and Graphical Processing Unit, respectively. These results highlight the capabilities of distributed large-scale edge computing over industrial scenarios. View Full-Text
Keywords: predictive maintenance; edge computing; artificial intelligence; internet of things; industry 4.0 predictive maintenance; edge computing; artificial intelligence; internet of things; industry 4.0
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MDPI and ACS Style

Resende, C.; Folgado, D.; Oliveira, J.; Franco, B.; Moreira, W.; Oliveira-Jr, A.; Cavaleiro, A.; Carvalho, R. TIP4.0: Industrial Internet of Things Platform for Predictive Maintenance. Sensors 2021, 21, 4676. https://doi.org/10.3390/s21144676

AMA Style

Resende C, Folgado D, Oliveira J, Franco B, Moreira W, Oliveira-Jr A, Cavaleiro A, Carvalho R. TIP4.0: Industrial Internet of Things Platform for Predictive Maintenance. Sensors. 2021; 21(14):4676. https://doi.org/10.3390/s21144676

Chicago/Turabian Style

Resende, Carlos, Duarte Folgado, João Oliveira, Bernardo Franco, Waldir Moreira, Antonio Oliveira-Jr, Armando Cavaleiro, and Ricardo Carvalho. 2021. "TIP4.0: Industrial Internet of Things Platform for Predictive Maintenance" Sensors 21, no. 14: 4676. https://doi.org/10.3390/s21144676

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