Paper Info
Title
Preventive Maintenance For Heterogeneous Industrial Vehicles With Incomplete Usage Data
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 charac-teristics 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 vehi-cles 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.& nbsp; (c) 2021 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.compind.2021.103468
COMPUTERS IN INDUSTRY
Keywords
DocType
Volume
Preventive maintenance, Industrial vehicles, Fleet management, Machine learning, Classification
Journal
130
ISSN
Citations 
PageRank 
0166-3615
0
0.34
References 
Authors
15
8
Name
Order
Citations
PageRank
Dena Markudova100.34
Sachit Mishra200.34
Luca Cagliero300.34
Luca Vassio43313.82
Marco Mellia52748204.65
Elena Baralis600.34
Lucia Salvatori722.40
Riccardo Loti800.34