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Scalable fleet monitoring and visualization for smart machine maintenance and industrial IoT applications

(2020) SENSORS. 20(15).
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Abstract
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.
Keywords
INTERNET, THINGS, fleet monitoring, bearing degradation, Cyber-Physical System, predictive, maintenance, Industrial Internet of Things, Industry 4, accelerated, lifetime testing

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MLA
Moens, Pieter, et al. “Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications.” SENSORS, vol. 20, no. 15, 2020, doi:10.3390/s20154308.
APA
Moens, P., Bracke, V., Soete, C., Vanden Hautte, S., Nieves Avendano, D., Ooijevaar, T., … Van Hoecke, S. (2020). Scalable fleet monitoring and visualization for smart machine maintenance and industrial IoT applications. SENSORS, 20(15). https://doi.org/10.3390/s20154308
Chicago author-date
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 (15). https://doi.org/10.3390/s20154308.
Chicago author-date (all authors)
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 (15). doi:10.3390/s20154308.
Vancouver
1.
Moens P, Bracke V, Soete C, Vanden Hautte S, Nieves Avendano D, Ooijevaar T, et al. Scalable fleet monitoring and visualization for smart machine maintenance and industrial IoT applications. SENSORS. 2020;20(15).
IEEE
[1]
P. Moens et al., “Scalable fleet monitoring and visualization for smart machine maintenance and industrial IoT applications,” SENSORS, vol. 20, no. 15, 2020.
@article{8673085,
  abstract     = {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.},
  articleno    = {4308},
  author       = {Moens, Pieter and Bracke, Vincent and Soete, Colin and Vanden Hautte, Sander and Nieves Avendano, Diego and Ooijevaar, Ted and Devos, Steven and Volckaert, Bruno and Van Hoecke, Sofie},
  issn         = {1424-8220},
  journal      = {SENSORS},
  keywords     = {INTERNET,THINGS,fleet monitoring,bearing degradation,Cyber-Physical System,predictive,maintenance,Industrial Internet of Things,Industry 4,accelerated,lifetime testing},
  language     = {eng},
  number       = {15},
  pages        = {15},
  title        = {Scalable fleet monitoring and visualization for smart machine maintenance and industrial IoT applications},
  url          = {http://dx.doi.org/10.3390/s20154308},
  volume       = {20},
  year         = {2020},
}

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