Industrial and construction vehicles require tight periodic maintenance operations. Their schedule depends on vehicle characteristics and usage. The latter can be accurately monitored through various on-board devices, enabling the application of Machine Learning techniques to analyze vehicle usage patterns and design predictive analytics. This paper presents a data-driven application to automatically schedule the periodic maintenance operations of industrial vehicles. It aims to predict, for each vehicle and date, the actual remaining days until the next maintenance is due. Our Machine Learning solution is designed to address the following challenges: (i) the non-stationarity of the per-vehicle utilization time series, which limits the effectiveness of classic scheduling policies, and (ii) the potential lack of historical data for those vehicles that have recently been added to the fleet, which hinders the learning of accurate predictors from past data. Preliminary results collected in a real industrial scenario demonstrate the effectiveness of the proposed solution on heterogeneous vehicles. The system we propose here is currently under deployment, enabling further tests and tunings.

Machine learning supported next-maintenance prediction for industrial vehicles / Mishra, S.; Vassio, L.; Cagliero, L.; Mellia, M.; Baralis, E.; Loti, R.; Salvatori, L.. - STAMPA. - 2578:(2020). ((Intervento presentato al convegno Workshops of the 23rd International Conference on Extending Database Technology/23rd International Conference on Database Theory, EDBT-ICDT-WS 2020 tenutosi a dnk nel 2020.

Machine learning supported next-maintenance prediction for industrial vehicles

Vassio L.;Cagliero L.;Mellia M.;Baralis E.;
2020

Abstract

Industrial and construction vehicles require tight periodic maintenance operations. Their schedule depends on vehicle characteristics and usage. The latter can be accurately monitored through various on-board devices, enabling the application of Machine Learning techniques to analyze vehicle usage patterns and design predictive analytics. This paper presents a data-driven application to automatically schedule the periodic maintenance operations of industrial vehicles. It aims to predict, for each vehicle and date, the actual remaining days until the next maintenance is due. Our Machine Learning solution is designed to address the following challenges: (i) the non-stationarity of the per-vehicle utilization time series, which limits the effectiveness of classic scheduling policies, and (ii) the potential lack of historical data for those vehicles that have recently been added to the fleet, which hinders the learning of accurate predictors from past data. Preliminary results collected in a real industrial scenario demonstrate the effectiveness of the proposed solution on heterogeneous vehicles. The system we propose here is currently under deployment, enabling further tests and tunings.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2838488