An E-Maintenance IoT Sensor Node Designed for Fleets of Diverse Heavy-Duty Vehicles
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32807
An E-Maintenance IoT Sensor Node Designed for Fleets of Diverse Heavy-Duty Vehicles

Authors: George Charkoftakis, Panagiotis Liosatos, Nicolas-Alexander Tatlas, Dimitrios Goustouridis, Stelios M. Potirakis

Abstract:

E-maintenance is a relatively recent concept, generally referring to maintenance management by monitoring assets over the Internet. One of the key links in the chain of an e-maintenance system is data acquisition and transmission. Specifically for the case of a fleet of heavy-duty vehicles, where the main challenge is the diversity of the vehicles and vehicle-embedded self-diagnostic/reporting technologies, the design of the data acquisition and transmission unit is a demanding task. This is clear if one takes into account that a heavy-vehicles fleet assortment may range from vehicles with only a limited number of analog sensors monitored by dashboard light indicators and gauges to vehicles with plethora of sensors monitored by a vehicle computer producing digital reporting. The present work proposes an adaptable internet of things (IoT) sensor node that is capable of addressing this challenge. The proposed sensor node architecture is based on the increasingly popular single-board computer – expansion boards approach. In the proposed solution, the expansion boards undertake the tasks of position identification, cellular connectivity, connectivity to the vehicle computer, and connectivity to analog and digital sensors by means of a specially targeted design of expansion board. Specifically, the latter offers a number of adaptability features to cope with the diverse sensor types employed in different vehicles. In standard mode, the IoT sensor node communicates to the data center through cellular network, transmitting all digital/digitized sensor data, IoT device identity and position. Moreover, the proposed IoT sensor node offers connectivity, through WiFi and an appropriate application, to smart phones or tablets allowing the registration of additional vehicle- and driver-specific information and these data are also forwarded to the data center. All control and communication tasks of the IoT sensor node are performed by dedicated firmware.

Keywords: IoT sensor nodes, e-maintenance, single-board computers, sensor expansion boards, on-board diagnostics

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 499

References:


[1] S. Mohammad, M. A. A. Masuri, S. Salim, and M. R. Abdul Razak, “Development of IoT Based Logistic Vehicle Maintenance System,” in Proc. IEEE 17th International Colloquium on Signal Processing & Its Applications (CSPA), Langkawi, Malaysia, 2021, pp. 127-132.
[2] 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.
[3] 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.
[4] 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.
[5] 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. https://doi.org/10.1007/978-3-319-66923-6_47
[6] 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. https://doi.org/10.1007/978-3-030-74154-9_1
[7] D. Goustouridis, A. Sideris, I. Sdrolias, G. Loizos, N. A. Tatlas, and S. M. Potirakis, “IntelligentLogger: A Heavy-Duty Vehicles Fleet Management System Based on IoT and Smart Prediction Techniques,” World Academy of Science, Engineering and Technology, Open Science Index 176, International Journal of Mechanical and Industrial Engineering, vol. 15(8), pp. 336 - 340, 2021. https://publications.waset.org/10012185/pdf
[8] A. BinMasoud and Q. Cheng, "Design of an IoT-based Vehicle State Monitoring System Using Raspberry Pi," in Proc. 2019 International Conference on Electrical Engineering Research & Practice (ICEERP), 2019, pp. 1-6.
[9] S. K. Singh, A. K. Singh, and A. Sharma “OBD - II based Intelligent Vehicular Diagnostic System using IoT,” in Proc. ISIC’21: International Semantic Intelligence Conference, Delhi, India, 2021, pp. 511-515.
[10] N. Goyal, V. Goel, M. Anand, and S. Garg, “Smart Vehicle: Online Prognosis for Vehicle Health Monitoring,” J. Innovation in Computer Sci. Eng., vol. 9, no. 2, pp. 12-22, Jan-June 2020.
[11] B. C. Nithin, S. Pooja, K. G. Sampath, and S. S. Sharmila, “On-Board Vehicle Fault Monitoring System”, pices, vol. 4, no. 5, pp. 82-84, Sep. 2020.
[12] S. Hussain, U. Mahmud and S. Yang, "Car e-Talk: An IoT-Enabled Cloud-Assisted Smart Fleet Maintenance System," IEEE Internet of Things J., vol. 8, no. 12, pp. 9484-9494, 15 June15, 2021.
[13] R. Barnes, “Raspberry Pi 3: Specs, benchmarks & testing,” The MagPi Magazine, 2016; https://magpi.raspberrypi.org/articles/raspberry-pi-3-specs-benchmarks (accessed on 26/06/2021).
[14] L. Hattersley, “Build a car computer 'carputer' with Raspberry Pi,” The MagPi Magazine, 2019; https://magpi.raspberrypi.org/articles/build-car-computer-raspberry-pi (accessed on 26/06/2021).