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Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications
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, e...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7435597/ https://www.ncbi.nlm.nih.gov/pubmed/32748809 http://dx.doi.org/10.3390/s20154308 |
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author | Moens, Pieter Bracke, Vincent Soete, Colin Vanden Hautte, Sander Nieves Avendano, Diego Ooijevaar, Ted Devos, Steven Volckaert, Bruno Van Hoecke, Sofie |
author_facet | Moens, Pieter Bracke, Vincent Soete, Colin Vanden Hautte, Sander Nieves Avendano, Diego Ooijevaar, Ted Devos, Steven Volckaert, Bruno Van Hoecke, Sofie |
author_sort | Moens, Pieter |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7435597 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-74355972020-08-28 Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications Moens, Pieter Bracke, Vincent Soete, Colin Vanden Hautte, Sander Nieves Avendano, Diego Ooijevaar, Ted Devos, Steven Volckaert, Bruno Van Hoecke, Sofie Sensors (Basel) Article 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. MDPI 2020-08-02 /pmc/articles/PMC7435597/ /pubmed/32748809 http://dx.doi.org/10.3390/s20154308 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Moens, Pieter Bracke, Vincent Soete, Colin Vanden Hautte, Sander Nieves Avendano, Diego Ooijevaar, Ted Devos, Steven Volckaert, Bruno Van Hoecke, Sofie Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications |
title | Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications |
title_full | Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications |
title_fullStr | Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications |
title_full_unstemmed | Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications |
title_short | Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications |
title_sort | scalable fleet monitoring and visualization for smart machine maintenance and industrial iot applications |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7435597/ https://www.ncbi.nlm.nih.gov/pubmed/32748809 http://dx.doi.org/10.3390/s20154308 |
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