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

作     者：Moens, Pieter Bracke, Vincent Soete, Colin Vanden Hautte, Sander Avendano, Diego Nieves Ooijevaar, Ted Devos, Steven Volckaert, Bruno Van Hoecke, Sofie 

作者机构：Ghent Univ imec IDLab Technol Pk Zwijnaarde 122 B-9052 Ghent Belgium Flanders Make Corelab Decis Celestijnenlaan 300 B-3001 Leuven Belgium 

出 版 物：《SENSORS》 (传感器)

年 卷 期：2020年第20卷第15期

页      面：4308.-4308页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 0808[工学-电气工程] 07[理学] 0804[工学-仪器科学与技术] 0703[理学-化学] 

基　　金：VLAIO 

主　　题：fleet monitoring bearing degradation Cyber-Physical System predictive maintenance Industrial Internet of Things Industry 4 0 accelerated lifetime testing 

摘      要：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.