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. 2020 Aug 2;20(15):4308.
doi: 10.3390/s20154308.

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

Pieter Moens  1 , Vincent Bracke  1 , Colin Soete  1 , Sander Vanden Hautte  1 , Diego Nieves Avendano  1 , Ted Ooijevaar  2 , Steven Devos  2 , Bruno Volckaert  1 , Sofie Van Hoecke  1
Affiliations

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

Pieter Moens et al. Sensors (Basel). .

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: Cyber-Physical System; Industrial Internet of Things; Industry 4.0; accelerated lifetime testing; bearing degradation; fleet monitoring; predictive maintenance.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Presented scalable Industrial Internet of Things architecture.
Figure 2
Figure 2
One of the seven setups that is used to accelerate the lifetime of bearings and allows to acquire data during the accumulation of operational bearing faults until end-of-life. Reprinted from IFAC-PapersOnLine, 52, Ooijevaar, T., et al., “Smart Machine Maintenance Enabled by a Condition Monitoring Living Lab”, 376–381, 2019, with permission from Elsevier.
Figure 3
Figure 3
Obelisk architecture. Combined, the blue and orange components form the Public API for the stakeholders (users and connected devices), accessible through the load balancer. The arrows indicate dependencies between the different components.
Figure 4
Figure 4
Dynamic Dashboard architecture. The orange components are core services within the dynamic dashboard, the blue components are provided by the platform tier, here Obelisk, and the green components are Web Things in the edge tier exposed according to the Web Thing Model.
Figure 5
Figure 5
Visualization within the dynamic dashboard application of KPIs from Setup 3 in the Smart Maintenance Living Lab as listed in Table 1.
See this image and copyright information in PMC

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