IoT-based predictive maintenance for fleet management
| Autor: | Tet Hin Yeap, Iluju Kiringa, Patrick Killeen, Bo Ding |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
business.industry
Computer science Big data 020206 networking & telecommunications 02 engineering and technology Predictive maintenance Reliability engineering Work (electrical) Public transport 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences 020201 artificial intelligence & image processing Internet of Things business General Environmental Science Fleet management |
| Zdroj: | ANT/EDI40 |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2019.04.184 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|