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Analysis of Predictive Maintenance for Tunnel Systems

Author

Listed:
  • Tomáš Tichý

    (Faculty of Transportation Sciences, Czech Technical University in Prague, 110 00 Prague, Czech Republic)

  • Jiří Brož

    (Faculty of Transportation Sciences, Czech Technical University in Prague, 110 00 Prague, Czech Republic)

  • Zuzana Bělinová

    (Faculty of Transportation Sciences, Czech Technical University in Prague, 110 00 Prague, Czech Republic)

  • Rastislav Pirník

    (Faculty of Electrical Engineering and Information Technology, University of Zilina, 010 26 Zilina, Slovakia)

Abstract

Smart and automated maintenance could make the system and its parts more sustainable by extending their lifecycle, failure detection, smart control of the equipment, and precise detection and reaction to unexpected circumstances. This article focuses on the analysis of data, particularly on logs captured in several Czech tunnel systems. The objective of the analysis is to find useful information in the logs for predicting upcoming situations, and furthermore, to check the possibilities of predictive diagnostics and to design the process of predictive maintenance. The main goal of the article is to summarize the possibilities of optimizing system maintenance that are based on data analysis as well as expert analysis based on the experience with the equipment in the tunnel. The results, findings, and conclusions could primarily be used in the tunnels; secondarily, these principles could be applied in telematics and lead to the optimization and improvement of system sustainability.

Suggested Citation

  • Tomáš Tichý & Jiří Brož & Zuzana Bělinová & Rastislav Pirník, 2021. "Analysis of Predictive Maintenance for Tunnel Systems," Sustainability, MDPI, vol. 13(7), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:7:p:3977-:d:529484
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    Citations

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    Cited by:

    1. Frantisek Kuda & Petr Dlask & Marek Teichmann & Vaclav Beran, 2022. "Time–Cost Schedules and Project–Threats Indication," Sustainability, MDPI, vol. 14(5), pages 1-16, February.
    2. Evgeny Burnaev & Evgeny Mironov & Aleksei Shpilman & Maxim Mironenko & Dmitry Katalevsky, 2023. "Practical AI Cases for Solving ESG Challenges," Sustainability, MDPI, vol. 15(17), pages 1-15, August.
    3. Pavol Kuchár & Rastislav Pirník & Tomáš Tichý & Karol Rástočný & Michal Skuba & Tamás Tettamanti, 2021. "Noninvasive Passenger Detection Comparison Using Thermal Imager and IP Cameras," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
    4. Jiří Brož & Tomáš Tichý & Radovan Prokeš & Adam Štencek & Tomáš Šmerda, 2023. "Proximity Approach to Bluetooth Low Energy-Based Localization in Tunnels," Sustainability, MDPI, vol. 15(4), pages 1-13, February.
    5. Xiangang Cao & Pengfei Li & Song Ming, 2021. "Remaining Useful Life Prediction-Based Maintenance Decision Model for Stochastic Deterioration Equipment under Data-Driven," Sustainability, MDPI, vol. 13(15), pages 1-19, July.
    6. Pavol Kuchár & Rastislav Pirník & Aleš Janota & Branislav Malobický & Jozef Kubík & Dana Šišmišová, 2023. "Passenger Occupancy Estimation in Vehicles: A Review of Current Methods and Research Challenges," Sustainability, MDPI, vol. 15(2), pages 1-27, January.

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