Fault-Tree-Analysis-Based Health Monitoring for Autonomous Underwater Vehicle

Sungil Byun; Mayorkinos Papaelias; Fausto Pedro García Márquez; Dongik Lee

doi:10.3390/jmse10121855

Fault-Tree-Analysis-Based Health Monitoring for Autonomous Underwater Vehicle

Sungil Byun, Mayorkinos Papaelias, Fausto Pedro García Márquez, Dongik Lee^*

^*Corresponding author for this work

Metallurgy and Materials

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

62 Downloads (Pure)

Abstract

Undersea terrain and resource exploration missions using autonomous underwater vehicles (AUVs) require a great deal of time. Therefore, it is necessary to monitor the state of the AUV in real time during the mission. In this paper, we propose an online health-monitoring method for AUVs using fault-tree analysis. The entire system is divided into four subsystems. Fault trees of each subsystem are designed based on the information of performance and reliability. Using the given subsystem fault trees, the health status of the entire system is evaluated by considering the performance, reliability, fault status, and weight factors of the parts. The effectiveness of the proposed method is demonstrated through simulations with various scenarios.

Original language	English
Article number	1855
Number of pages	15
Journal	Journal of Marine Science and Engineering
Volume	10
Issue number	12
DOIs	https://doi.org/10.3390/jmse10121855
Publication status	Published - 2 Dec 2022

Bibliographical note

Funding Information:
This research was supported by Unmanned Vehicles Core Technology Research and Development Program through the National Research Foundation of Korea (NRF), Unmanned Vehicle Advanced Research Center (UVARC) funded by the Ministry of Science and ICT, the Republic of Korea (NRF-2020M3C1C1A02086313), and it was supported by the International Research & Development Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (Grant number: NRF-2018K1A3A7A03089832). The ENDURUNS project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (Research Grant Agreement H2020-MG-2018-2019-2020 n.824348).

Publisher Copyright:
© 2022 by the authors.

Keywords

autonomous underwater vehicle
failure
fault-tree analysis
performance analysis
reliability

ASJC Scopus subject areas

Civil and Structural Engineering
Water Science and Technology
Ocean Engineering

Access to Document

10.3390/jmse10121855Licence: Creative Commons: Attribution (CC BY)

ByunS2022FaultFinal published version, 2.74 MBLicence: Creative Commons: Attribution (CC BY)

H2020_RIA_ENDURUNS
Atkins, P. (Co-Investigator), Book, D. (Co-Investigator), Papaelias, M. (Principal Investigator) & Fernando, G. (Co-Investigator)
European Commission
1/11/18 → 31/07/23
Project: EU

Cite this

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title = "Fault-Tree-Analysis-Based Health Monitoring for Autonomous Underwater Vehicle",

abstract = "Undersea terrain and resource exploration missions using autonomous underwater vehicles (AUVs) require a great deal of time. Therefore, it is necessary to monitor the state of the AUV in real time during the mission. In this paper, we propose an online health-monitoring method for AUVs using fault-tree analysis. The entire system is divided into four subsystems. Fault trees of each subsystem are designed based on the information of performance and reliability. Using the given subsystem fault trees, the health status of the entire system is evaluated by considering the performance, reliability, fault status, and weight factors of the parts. The effectiveness of the proposed method is demonstrated through simulations with various scenarios.",

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note = "Funding Information: This research was supported by Unmanned Vehicles Core Technology Research and Development Program through the National Research Foundation of Korea (NRF), Unmanned Vehicle Advanced Research Center (UVARC) funded by the Ministry of Science and ICT, the Republic of Korea (NRF-2020M3C1C1A02086313), and it was supported by the International Research \& Development Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT \& Future Planning (Grant number: NRF-2018K1A3A7A03089832). The ENDURUNS project has received funding from the European Research Council (ERC) under the European Union{\textquoteright}s Horizon 2020 Research and Innovation Programme (Research Grant Agreement H2020-MG-2018-2019-2020 n.824348). Publisher Copyright: {\textcopyright} 2022 by the authors.",

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N1 - Funding Information: This research was supported by Unmanned Vehicles Core Technology Research and Development Program through the National Research Foundation of Korea (NRF), Unmanned Vehicle Advanced Research Center (UVARC) funded by the Ministry of Science and ICT, the Republic of Korea (NRF-2020M3C1C1A02086313), and it was supported by the International Research & Development Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (Grant number: NRF-2018K1A3A7A03089832). The ENDURUNS project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (Research Grant Agreement H2020-MG-2018-2019-2020 n.824348). Publisher Copyright: © 2022 by the authors.

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N2 - Undersea terrain and resource exploration missions using autonomous underwater vehicles (AUVs) require a great deal of time. Therefore, it is necessary to monitor the state of the AUV in real time during the mission. In this paper, we propose an online health-monitoring method for AUVs using fault-tree analysis. The entire system is divided into four subsystems. Fault trees of each subsystem are designed based on the information of performance and reliability. Using the given subsystem fault trees, the health status of the entire system is evaluated by considering the performance, reliability, fault status, and weight factors of the parts. The effectiveness of the proposed method is demonstrated through simulations with various scenarios.

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Fault-Tree-Analysis-Based Health Monitoring for Autonomous Underwater Vehicle

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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