Risk analysis methodology using STPA-based Bayesian network- applied to remote pilotage operation

Basnet, Sunil; BahooToroody, Ahmad; Chaal, Meriam; Lahtinen, Jussi; Bolbot, Victor; Banda, Osiris A. Valdez

doi:10.1016/j.oceaneng.2022.113569

Cited by 29 publications

(9 citation statements)

References 51 publications

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“…Furthermore, the methodology uses Noisy-OR gates to determine the conditional probabilities of the BN variables. The Noisy-OR gates can reduce the modeling and computational complexities of the large BN models (BayesFusion 2020; Pearl 1988), which has been demonstrated in several studies such as Basnet et al (2023); Abaei et al (2019); Ji et al (2022).…”

Section: Introductionmentioning

confidence: 94%

“…The ongoing development of remote pilotage in European countries allows pilots to assist ship masters from shore. It is expected that in remote pilotage, there will be many common systems and processes with the current pilotage operations (Basnet et al 2023). Hence, it is crucial to analyze risk factors in ship pilotage to improve the implementation of remote pilotage in the future.…”

Section: Descriptionmentioning

confidence: 99%

“…As a result, maritime stakeholders should monitor and manage risks in such an important and safety-critical field. Furthermore, the developments of new services such as Remote pilotage and autonomous ships have increased the system complexity due to increased software control and higher systemic interactions (Lahtinen et al 2020;Basnet et al 2023). Hence, safety engineers are striving to develop and implement novel risk monitoring and mitigating methods/tools that can handle the increased system complexity.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Real-Time Risk Monitoring of Ship Pilotage Operations: Automating BN Risk Model Development

Basnet,

BahooToroody,

Bolbot

et al. 2023

Proceeding of the 33rd European Safety and Reliability Conference

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The maritime industry is evolving further with new technologies and services, such as autonomous ships and remote pilotage operations, under development. Whilst these services may bring new opportunities and benefits, the ability to manage risks becomes increasingly vital. Furthermore, these new technologies and services require risk models of a dynamic nature, which can incorporate ongoing systemic changes and provide risk estimation in real time. Hence, this paper presents a novel approach, which extracts the incident data and automatically establishes a realtime Bayesian risk model. The model consists of a clear hierarchy denoting a chain of risk events, i.e., root causes, hazards, accidents, and losses. The resulting risk model provides an estimation of the posterior probability of occurrence of all variables in the Bayesian network. These results are next plotted and monitored with Graphical user interface application to monitor the critical factors leading to losses and thus requiring risk control in real time. The effectiveness of the method is afterward demonstrated using a case study of ship pilotage operations. The resulted model shows the probability of occurrence of risk events such as accidents, losses, hazardous scenarios and causal factors.

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Section: Introductionmentioning

confidence: 94%

Section: Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Real-Time Risk Monitoring of Ship Pilotage Operations: Automating BN Risk Model Development

Basnet,

BahooToroody,

Bolbot

et al. 2023

Proceeding of the 33rd European Safety and Reliability Conference

View full text Add to dashboard Cite

show abstract

“…There are only a few articles mentioning 'Smart Fairways' [7,[21][22][23][24][25][26] or 'intelligent fairways' [7,[27][28][29][30][31]. While these studies explore various aspects related to fairways, they do not provide a precise definition of a 'Smart Fairway'.…”

Section: Literaturementioning

confidence: 99%

“…These studies, instead, focus on different topics. These include risk management for smart shipping services [29,31]; network connectivity for communications ranging from low-priority entertainment to highpriority safety critical [22,[32][33][34]; simulation of local wind conditions [23]; platforms [7]; and data-driven decision making in maritime transport and logistics [24]. They also analyze and develop methods for safeguarding systems against cyber threats [21,25,26,28].…”

Section: Literaturementioning

confidence: 99%

Navigating the Future: Developing Smart Fairways for Enhanced Maritime Safety and Efficiency

Heikkilä,

Himmanen,

Soininen

et al. 2024

JMSE

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The maritime industry is rapidly evolving with digital technologies, aiming to enhance efficiency, safety, and sustainability. Recent interest has focused on autonomous vessels and the digitalization of ports, yet fairway development has lagged behind. To effectively support the growing digital and autonomous marine traffic, it is essential that fairways are also upgraded and modernized. Addressing this need, this study examines key elements of Smart Fairways, with a particular focus on Finland’s maritime infrastructure. This research contributes to the development of the Smart Fairways concept by identifying five foundational and ten advanced Smart Fairway service elements. The main finding highlights the foundational role of communication systems in the development of more advanced Smart Fairway services such as Enhanced Vessel Traffic Service, Port just-in-time Service, Remote Pilotage, and Digital Twin of the Physical Fairway.

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Quantitative Risk Assessment for Autonomous Vehicles: Integrating Functional Resonance Analysis Method and Bayesian Network

Deng,

Li,

Liu

et al. 2024

Quality & Reliability Eng

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In autonomous vehicles (AVs), intricate functional‐level couplings exist among the components. Accidents can occur even when all functions are operating normally, as subtle performance variabilities in these functions can aggregate through these couplings, leading to functional resonance. The aim of this study is to identify, analyze and quantitatively assess the safety issues caused by these complex interactions in AVs and to propose appropriate risk management strategies to improve vehicle safety. Commonly used modern methods of risk assessment, such as system‐theoretical process analysis and accident mapping, struggle to capture this resonance in AVs and lack quantitative analysis. To this end, this paper proposes a quantitative risk assessment method that integrates functional resonance analysis method (FRAM) with Bayesian network (BN) to reveal the complex interactions and quantify risks within AVs. Initially, a FRAM model is constructed to characterize the function couplings of a system, which are subsequently aggregated into functional resonance chains to identify potential hazards. Then, these functional resonance chains are used to develop a BN model for quantitative assessment of system risk. A case study of an automatic emergency braking (AEB) system on an open‐source vehicle is conducted to verify its effectiveness. The results demonstrate that the proposed approach not only identifies functional resonance but also effectively quantifies risks in the AEB system.

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Risk analysis methodology using STPA-based Bayesian network- applied to remote pilotage operation

Cited by 29 publications

References 51 publications

Real-Time Risk Monitoring of Ship Pilotage Operations: Automating BN Risk Model Development

Real-Time Risk Monitoring of Ship Pilotage Operations: Automating BN Risk Model Development

Navigating the Future: Developing Smart Fairways for Enhanced Maritime Safety and Efficiency

Quantitative Risk Assessment for Autonomous Vehicles: Integrating Functional Resonance Analysis Method and Bayesian Network

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