Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32727
IntelligentLogger: A Heavy-Duty Vehicles Fleet Management System Based on IoT and Smart Prediction Techniques

Authors: D. Goustouridis, A. Sideris, I. Sdrolias, G. Loizos, N.-Alexander Tatlas, S. M. Potirakis


Both daily and long-term management of a heavy-duty vehicles and construction machinery fleet is an extremely complicated and hard to solve issue. This is mainly due to the diversity of the fleet vehicles – machinery, which concerns not only the vehicle types, but also their age/efficiency, as well as the fleet volume, which is often of the order of hundreds or even thousands of vehicles/machineries. In the present paper we present “InteligentLogger”, a holistic heavy-duty fleet management system covering a wide range of diverse fleet vehicles. This is based on specifically designed hardware and software for the automated vehicle health status and operational cost monitoring, for smart maintenance. InteligentLogger is characterized by high adaptability that permits to be tailored to practically any heavy-duty vehicle/machinery (of different technologies -modern or legacy- and of dissimilar uses). Contrary to conventional logistic systems, which are characterized by raised operational costs and often errors, InteligentLogger provides a cost-effective and reliable integrated solution for the e-management and e-maintenance of the fleet members. The InteligentLogger system offers the following unique features that guarantee successful heavy-duty vehicles/machineries fleet management: (a) Recording and storage of operating data of motorized construction machinery, in a reliable way and in real time, using specifically designed Internet of Things (IoT) sensor nodes that communicate through the available network infrastructures, e.g., 3G/LTE; (b) Use on any machine, regardless of its age, in a universal way; (c) Flexibility and complete customization both in terms of data collection, integration with 3rd party systems, as well as in terms of processing and drawing conclusions; (d) Validation, error reporting & correction, as well as update of the system’s database; (e) Artificial intelligence (AI) software, for processing information in real time, identifying out-of-normal behavior and generating alerts; (f) A MicroStrategy based enterprise BI, for modeling information and producing reports, dashboards, and alerts focusing on vehicles– machinery optimal usage, as well as maintenance and scraping policies; (g) Modular structure that allows low implementation costs in the basic fully functional version, but offers scalability without requiring a complete system upgrade.

Keywords: E-maintenance, predictive maintenance, IoT sensor nodes, cost optimization, artificial intelligence, heavy-duty vehicles.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 664


[1] B. Iung, E. Levrat, A. Crespo Marquez and H. Erbe, “Conceptual framework for e-Maintenance: Illustration by e-Maintenance technologies and platforms,” Ann. Rev. Control, vol. 33, pp. 220-229, 2009.
[2] E. Levrat, B. Iung and A. Crespo Marquez, “E-maintenance: review and conceptual framework,” Prod. Plan. Control, vol. 19, no. 4, pp. 408-429, 2008.
[3] A. Muller, A. Crespo Marquez and B. Iung, “On the concept of e-maintenance: Review and current research,” Reliability Eng. Syst. Safety, vol. 93, pp. 1165-1187, 2008.
[4] A. Bousdekis and G. Mentzas, “Condition-Based Predictive Maintenance in the Frame of Industry 4.0,” in Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing, H. Lödding et al. (eds.), Cham: Springer, 2017, pp. 399-406.
[5] R. S. Velmurugan and T. Dhingra, “Asset Maintenance: A Primary Support Function,” in Asset Maintenance Management in Industry, R. S. Velmurugan and T. Dhingra (eds.), Cham: Springer, 2021, pp. 1-21.
[6] Y. Song, F. R. Yu, L. Zhou, X. Yang and Z. He, "Applications of the Internet of Things (IoT) in Smart Logistics: A Comprehensive Survey," IEEE Internet of Things J., vol. 8, no. 6, pp. 4250-4274, 2021.
[10] R. Kimball and M. Ross, The Data Warehouse Toolkit. Hoboken, NJ: John Wiley & Sons, Inc., 2013.
[11], item III.