Unsupervised Dynamic Sensor Selection for IoT-Based Predictive Maintenance of a Fleet of Public Transport Buses

Abstract

In recent years, big data produced by the Internet of Things (IoT) has enabled new kinds of useful applications. One such application is monitoring a fleet of vehicles in real-time to predict their remaining useful life. Consensus self-organized models (COSMO) approach is an example of a predictive maintenance system. The present work proposes a novel IoT-based architecture for predictive maintenance that consists of three primary nodes: namely, the vehicle node (VN), the server leader node (SLN), and the root node (RN), which enable on-board vehicle data processing, heavy-duty data processing, and fleet administration, respectively. A minimally viable prototype (MVP) of the proposed architecture was implemented and deployed to a local bus garage in Gatineau, Canada. The present work proposes an improved COSMO (ICOSMO), a fleet-wide unsupervised dynamic sensor selection algorithm. To analyze the performance of ICOSMO, a fleet simulation was implemented. The J1939 data gathered from a hybrid bus was used to generate synthetic data in the simulations. Simulation results that compared the performance of the COSMO and ICOSMO approaches revealed that in general ICOSMO improves the average area under the curve of COSMO by approximately 1.5% when using the Cosine distance and 0.6% when using the Hellinger distance.

Mobility Control Centre and Artificial Intelligence for Sustainable Urban Districts

“…By processing both traffic data and contextual events and integrating all the relevant information about the territory, the MCC can support, through artificial intelligence algorithms, intermodal, dynamic, and customized Infomobility for users [71].…”