IoT-based predictive maintenance for fleet management

被引:34
|
作者
Killeen, Patrick [1 ]
Ding, Bo [2 ]
Kiringa, Iluju [1 ]
Yeap, Tet [1 ]
机构
[1] Univ Ottawa, 75 Laurier Ave E, Ottawa, ON K1N 6N5, Canada
[2] Harbin Univ Sci & Technol, Harbin, Peoples R China
关键词
J1939; internet of things; predictive maintenance; sensor selection; fleet management; predictive analytics;
D O I
10.1016/j.procs.2019.04.184
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In recent years, the Internet of Things (IoT) and big data have been hot topics. With all this data being produced, new applications such as predictive maintenance are possible. Consensus self-organized models approach (COSMO) is an example of a predictive maintenance system for a fleet of public transport buses, which attempts to diagnose faulty buses that deviate from the rest of the bus fleet. The present work proposes a novel IoT architecture for predictive maintenance and proposes a semi-supervised machine learning algorithm that attempts to improve the sensor selection performed in COSMO. With the help of the Societe de Transport de l'Outaouais, a minimally viable prototype of the architecture has been deployed and J1939 sensor data have been acquired. (C) 2019 The Authors. Published by Elsevier B.V.
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页码:607 / 613
页数:7
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