Integrating Software FMEA and STPA to Develop a Bayesian Network-Based Software Risk Model for Autonomous Ships

The autonomous shipping industry is increasingly focusing on enhancing the safety and reliability of software-based systems. Conducting a risk assessment is a requirement for demonstrating the safety equivalence of autonomous ships based on such systems to conventional vessels. Traditional risk asse...

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Bibliographic Details
Published in:Journal of marine science and engineering 2024-01, Vol.12 (1), p.4
Main Authors: Yang, Xue, Zhu, Yawei, Zhou, Tao, Xu, Sheng, Zhang, Wenjun, Zhou, Xiangyu, Meng, Xiangkun
Format: Article
Language:English
Subjects:
autonomous ship
Bayesian analysis
Bayesian theory
Case studies
Container ships
Failure
Failure modes
Failures
Mathematical models
Methods
Navigation systems
Probability theory
Risk assessment
Safety
Shipping
Shipping industry
Ships
Software
Software FMEA
Software reliability
software risk assessment
Standardization
STPA
System reliability
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Summary:The autonomous shipping industry is increasingly focusing on enhancing the safety and reliability of software-based systems. Conducting a risk assessment is a requirement for demonstrating the safety equivalence of autonomous ships based on such systems to conventional vessels. Traditional risk assessment models, however, primarily focus on hardware failures, often overlooking potential software-related failures and functional inadequacies. This study proposes a framework integrating Software Failure Mode and Effects Analysis (FMEA), System–Theoretic Process Analysis (STPA), and Bayesian Network (BN) for risk identification of autonomous ship software systems. The results of a case study reveal that the framework sufficiently addresses the multifaceted nature of risks related to software in autonomous ships. Based on the findings of this study, we suggest the need for standardization of software architecture development in the autonomous ship industry and highlight the necessity for an enhanced understanding of AI-specific risks and the development of tailored risk assessment methodologies.
ISSN:2077-1312
2077-1312
DOI:10.3390/jmse12010004