A Hybrid Fuzzy System Dynamics Approach for Risk Analysis of AUV Operations

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 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.

A Review of Risk Analysis Research for the Operations of Autonomous Underwater Vehicles

“…Qualitative Safety layer method (Ortiz et al, 1999) Semiquantitative Risk management process (Griffiths and Trembanis, 2007;Brito et al, 2010;Griffiths and Brito, 2011;Thieme et al, 2015a) Fault tree analysis (Bian et al, 2009a, b;Hu et al, 2013;Xu et al, 2013;Aslansefat et al, 2014;Thieme et al, 2015a;Brito, 2016;Harris et al, 2016;Xiang et al, 2017;Brito and Chang, 2018) Event tree analysis (Thieme et al, 2015a; Failure Mode and Effects Analysis (Hu et al, 2013;Harris et al, 2016) Bow-tie model (Yu et al, 2017) Kaplan-Meier survival model (Brito et al, 2010;Brito et al, 2014a;Brito and Griffiths, 2016) Fuzzy set theory (Loh et al, 2019;Loh et al, 2020a;Loh et al, 2020b;Xu et al, 2020) Quantitative Bayesian belief network (Griffiths and Brito, 2008;Thieme et al, 2015b;Brito and Griffiths, 2016;Hegde et al, 2018;Bremnes et al, 2019) Markov chain (Brito and Griffiths, 2011;Griffiths and Brito, 2011) System dynamics Loh et al, 2020a;Loh et al, 2020c, b;Xu et al, 2020) Fig. 7.…”

“…Detailed information of human factors, such as level of training, operator experience, operator fatigue, and situation awareness, were analyzed in these studies. Furthermore, a system-based risk analysis framework was proposed for an in-depth analysis of the impact of human factors (Loh et al, 2019;Loh et al, 2020a;Loh et al, 2020b;Xu et al, 2020). Several key findings were demonstrated by these studies.…”

“…( Parasuraman and Miller, 2004;Ho et al, 2011;Fouse et al, 2012;Thieme et al, 2015b) Experience of operators Level of experience of the operators with the deployment mission. (Manley, 2007;Loh et al, 2020a;Loh et al, 2020c, b) Human fatigue…”

“…However, such uncertainties cannot be explicated expressed by the deterministic SD model. This limitation has promoted the recent development of integrating the SD method with fuzzy logic (Loh et al, 2020a;Loh et al, 2020b;Xu et al, 2020). A resultant fuzzy system dynamics risk analysis (FuSDRA) method was proposed to achieve a more robust risk for an AUV loss accident (Loh et al, 2020b).…”

“…At the same time, fuzzy logic was integrated to account for stochastic uncertainties of risk variables and their interrelationships. An extended study used the FuSDRA approach for exploring the relationships between AUV crews' experience and the risk of AUV loss (Loh et al, 2020a). It was the first time that the FuSDRA method was utilized for in-depth risk analysis of human factors.…”