Reliability analysis based on dynamic Bayesian networks: A case study of an unmanned surface vessel

Unmanned surface vessels (USVs) have been widely used in various activities to date. Reliability is particularly significant for USVs because of the increasing complexity of USV tasks and their characteristic that cannot be repaired during task execution. This study aims to analyse the reliability of USVs' overall system for providing an optimisation reference for their reliability improvement in the design phase. The method for modelling and analysing the reliability of complex systems based on Dynamic Bayesian Networks (DBNs) is studied. The reliability of a USV for seafloor topography survey is analysed. The reliability model of the USV overall system is established by mapping the Dynamic Fault Trees (DFTs) into DBNs considering the system logical structure, redundant configuration and dynamic behaviour. Reliability analysis, including reliability evaluation, importance measure and sensitivity analysis, are conducted on the basis of DBN inference. The overall reliability parameters of the USV are obtained to determine the critical subsystem and components. This research may provide a reference for the reliability design and optimisation of USVs.

• Record URL:
  https://doi.org/10.1016/j.oceaneng.2021.109970
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0029801821013123
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00298018
• Supplemental Notes:
  • © 2021 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Gao, Chao
  • Guo, Yongjin
  • Zhong, Mingjun
  • Liang, Xiaofeng
  • Wang, Hongdong
  • Yi, Hong
• Publication Date: 2021-11-15

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Serial:
  • Ocean Engineering
  • Volume: 240
  • Issue Number: 0
  • Publisher: Pergamon
  • ISSN: 0029-8018
  • EISSN: 1873-5258
  • Serial URL: http://www.sciencedirect.com/science/journal/00298018

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Fault tree analysis; Reliability; Sensitivity analysis; Ships
• Identifier Terms: Dynamic Bayesian Network
• Subject Areas: Marine Transportation; Vehicles and Equipment;

Filing Info

• Accession Number: 01785428
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 25 2021 9:16AM