eCite Digital Repository

A hybrid fuzzy system dynamics approach for risk analysis of AUV operations

Citation

Loh, TY and Brito, MP and Bose, N and Xu, J and Nikolova, N and Tenekedjiev, K, A hybrid fuzzy system dynamics approach for risk analysis of AUV operations, Journal of Advanced Computational Intelligence and Intelligent Informatics, 24, (1) pp. 1-14. ISSN 1343-0130 (2020) [Refereed Article]

Copyright Statement

Copyright © 2020 Fuji Technology Press Ltd.

DOI: doi:10.20965/jaciii.2020.p0026

Abstract

The maturing of autonomous technology has fostered a rapid expansion in the use of Autonomous Underwater Vehicles (AUVs). To prevent the loss of AUVs during deployments, existing risk analysis approaches tend to focus on technicalities, historical data and expertsí opinion for probability quantification. However, data may not always be available and the complex interrelationships between risk factors are often neglected due to uncertainties. To overcome these shortfalls, a hybrid fuzzy system dynamics risk analysis (FuSDRA) is proposed. The approach utilises the strengths while overcoming limitations of both system dynamics and fuzzy set theory. Presented as a threestep iterative framework, the approach was applied on a case study to examine the impact of crew operating experience on the risk of AUV loss. Results showed not only that initial experience of the team affects the risk of loss, but any loss of experience in earlier stages of the AUV program have a lesser impact as compared to later stages. A series of risk control policies were recommended based on the results. The case study demonstrated how the FuSDRA approach can be applied to inform human resource and risk management strategies, or broader application within the AUV domain and other complex technological systems.

Item Details

Item Type:Refereed Article
Keywords:AUV, risk management, fuzzy logic, simulation
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Neural, Evolutionary and Fuzzy Computation
Objective Division:Environment
Objective Group:Land and Water Management
Objective Field:Antarctic and Sub-Antarctic Land and Water Management
UTAS Author:Loh, TY (Mr Tzu Yang Loh)
UTAS Author:Nikolova, N (Professor Nataliya Nikolova)
UTAS Author:Tenekedjiev, K (Professor Kiril Tenekedjiev)
ID Code:135997
Year Published:2020
Deposited By:Maritime and Logistics Management
Deposited On:2019-11-25
Last Modified:2020-03-03
Downloads:0

Repository Staff Only: item control page