IoT-based predictive maintenance for fleet management

TLDR

IoT and big data enable predictive maintenance, exemplified by COSMO, which diagnoses faulty buses in public transport fleets. The study proposes a novel IoT architecture for predictive maintenance and a semi‑supervised machine‑learning algorithm to enhance sensor selection in COSMO. The architecture integrates IoT sensors with a semi‑supervised ML algorithm that refines sensor selection for fleet diagnostics. A minimally viable prototype was deployed with the Société de Transport de l’Outaouais, collecting J1939 sensor data.

Abstract

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.