Large fleets of industrial and construction vehicles require periodic maintenance activities. Scheduling these operations is potentially challenging because the optimal timeline depends on the vehicle characteristics and usage. This paper studies a real industrial case study, where a company providing telematics services supports fleet managers in scheduling maintenance operations of about 2000 construction vehicles of various types. The heterogeneity of the fleet and the availability of historical data fosters the use of data-driven solutions based on machine learning techniques. The paper addresses the learning of per-vehicle predictors aimed at forecasting the next-day utilisation level and the remaining time until the next maintenance. We explore the performance of both linear and non-liner models, showing that machine learning models are able to capture the underlying trends describing non-stationary vehicle usage patterns. We also explicitly consider the lack of data for vehicles that have been recently added to the fleet. Results show that the availability of even a limited portion of past utilisation levels enables the identification of vehicles with similar usage trends and the opportunistic reuse of their historical data.

Preventive maintenance for heterogeneous industrial vehicles with incomplete usage data / Markudova, Dena; Mishra, Sachit; Cagliero, Luca; Vassio, Luca; Mellia, Marco; Baralis, Elena; Salvatori, Lucia; Loti, Riccardo. - In: COMPUTERS IN INDUSTRY. - ISSN 0166-3615. - ELETTRONICO. - 130:(2021), p. 103468. [10.1016/j.compind.2021.103468]

Preventive maintenance for heterogeneous industrial vehicles with incomplete usage data

Markudova, Dena;Cagliero, Luca;Vassio, Luca;Mellia, Marco;Baralis, Elena;
2021

Abstract

Large fleets of industrial and construction vehicles require periodic maintenance activities. Scheduling these operations is potentially challenging because the optimal timeline depends on the vehicle characteristics and usage. This paper studies a real industrial case study, where a company providing telematics services supports fleet managers in scheduling maintenance operations of about 2000 construction vehicles of various types. The heterogeneity of the fleet and the availability of historical data fosters the use of data-driven solutions based on machine learning techniques. The paper addresses the learning of per-vehicle predictors aimed at forecasting the next-day utilisation level and the remaining time until the next maintenance. We explore the performance of both linear and non-liner models, showing that machine learning models are able to capture the underlying trends describing non-stationary vehicle usage patterns. We also explicitly consider the lack of data for vehicles that have been recently added to the fleet. Results show that the availability of even a limited portion of past utilisation levels enables the identification of vehicles with similar usage trends and the opportunistic reuse of their historical data.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2897946