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

Loh, Tzu Yang; Brito, Mario P.; Bose, Neil; Xu, Jingjing; Nikolova, Natalia; Tenekedjiev, Kiril

Abstract

© 2020 Fuji Technology Press. All rights reserved. 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 experts’ 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 three-step 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.

Citation

Loh, T. Y., Brito, M. P., Bose, N., Xu, J., Nikolova, N., & Tenekedjiev, K. (2020). A hybrid fuzzy system dynamics approach for risk analysis of AUV operations. Journal of Advanced Computational Intelligence and Intelligent Informatics, 24(1), 26-39. https://doi.org/10.20965/jaciii.2020.p0026

• Journal Article Type: Article
• Acceptance Date: Nov 11, 2019
• Online Publication Date: Jan 20, 2020
• Publication Date: Jan 20, 2020
• Deposit Date: Mar 7, 2020
• Journal: Journal of Advanced Computational Intelligence and Intelligent Informatics
• Print ISSN: 1343-0130
• Electronic ISSN: 1883-8014
• Peer Reviewed: Peer Reviewed
• Volume: 24
• Issue: 1
• Pages: 26-39
• DOI: https://doi.org/10.20965/jaciii.2020.p0026
• Keywords: Human-Computer Interaction; Artificial Intelligence; Computer Vision and Pattern Recognition
• Public URL: https://uwe-repository.worktribe.com/output/5586616
• Related Public URLs: https://eprints.soton.ac.uk/433810/