A novel failure mode and effects analysis model enhanced with systems theory and artificial intelligence for dynamic positioning systems in offshore operations
Abstract
Dynamic positioning (DP) system failures during deep-sea operations can lead to severe accidents, including blowouts and environmental damage. Performing reliable failure mode and effects analysis (FMEA) is therefore essential for ensuring the safety of offshore assets. However, traditional FMEA struggles with the inherent complexity and uncertainty of DP systems in the marine environment. To address these gaps, this paper proposes an enhanced T-spherical fuzzy FMEA model integrating systems theory and artificial intelligence techniques. The systems theory process analysis method is introduced to transcend the component-centric perspective, and effectively capture system-level hidden failure modes. The artificial intelligence enhanced risk perception data fusion and weight allocation method is constructed based on T-spherical fuzzy theory, to address cognitive uncertainty and overcome subjectivity drawbacks. A robust ranking and classification framework combining the alternative-by-alternative comparison method with K-means clustering to prevent ranking reversal and enable automatic risk grading. A case study of riserless light well intervention vessels shows that the systemic failures and control loop failures, rather than isolated hardware failures, constitute the primary risks in modern DP systems. Furthermore, the results indicate that the proposed model exhibits strong robustness and practical applicability, providing a valuable decision-support tool for safety management of complex deep-sea equipment.