| Autor: |
Killeen, Patrick, Ding, Bo, Kiringa, Iluju, Yeap, Tet |
| Předmět: |
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| Zdroj: |
Procedia Computer Science; 2020, Vol. 171, p607-613, 7p |
| Abstrakt: |
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 Société de Transport de l'Outaouais, a minimally viable prototype of the architecture has been deployed and J1939 sensor data have been acquired. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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