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Article

Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications

1
IDLab, Ghent University—imec, Technologiepark-Zwijnaarde 122, 9052 Gent, Belgium
2
Corelab DecisionS, Flanders Make, Celestijnenlaan 300, 3001 Leuven, Belgium
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(15), 4308; https://doi.org/10.3390/s20154308
Received: 30 June 2020 / Revised: 20 July 2020 / Accepted: 31 July 2020 / Published: 2 August 2020
(This article belongs to the Special Issue Intelligent Sensors in the Industry 4.0 and Smart Factory)
The wide adoption of smart machine maintenance in manufacturing is blocked by open challenges in the Industrial Internet of Things (IIoT) with regard to robustness, scalability and security. Solving these challenges is of uttermost importance to mission-critical industrial operations. Furthermore, effective application of predictive maintenance requires well-trained machine learning algorithms which on their turn require high volumes of reliable data. This paper addresses both challenges and presents the Smart Maintenance Living Lab, an open test and research platform that consists of a fleet of drivetrain systems for accelerated lifetime tests of rolling-element bearings, a scalable IoT middleware cloud platform for reliable data ingestion and persistence, and a dynamic dashboard application for fleet monitoring and visualization. Each individual component within the presented system is discussed and validated, demonstrating the feasibility of IIoT applications for smart machine maintenance. The resulting platform provides benchmark data for the improvement of machine learning algorithms, gives insights into the design, implementation and validation of a complete architecture for IIoT applications with specific requirements concerning robustness, scalability and security and therefore reduces the reticence in the industry to widely adopt these technologies. View Full-Text
Keywords: fleet monitoring; bearing degradation; Cyber-Physical System; predictive maintenance; Industrial Internet of Things; Industry 4.0; accelerated lifetime testing fleet monitoring; bearing degradation; Cyber-Physical System; predictive maintenance; Industrial Internet of Things; Industry 4.0; accelerated lifetime testing
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MDPI and ACS Style

Moens, P.; Bracke, V.; Soete, C.; Vanden Hautte, S.; Nieves Avendano, D.; Ooijevaar, T.; Devos, S.; Volckaert, B.; Van Hoecke, S. Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications. Sensors 2020, 20, 4308. https://doi.org/10.3390/s20154308

AMA Style

Moens P, Bracke V, Soete C, Vanden Hautte S, Nieves Avendano D, Ooijevaar T, Devos S, Volckaert B, Van Hoecke S. Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications. Sensors. 2020; 20(15):4308. https://doi.org/10.3390/s20154308

Chicago/Turabian Style

Moens, Pieter, Vincent Bracke, Colin Soete, Sander Vanden Hautte, Diego Nieves Avendano, Ted Ooijevaar, Steven Devos, Bruno Volckaert, and Sofie Van Hoecke. 2020. "Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications" Sensors 20, no. 15: 4308. https://doi.org/10.3390/s20154308

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