A review of failure modes in dynamic positioning vessel operations

Dynamic positioning (DP) is a key enabling technology for offshore drilling, subsea construction, and renewable energy operations, yet loss-of-position incidents continue to pose significant safety, environmental, and financial risks. This paper reviews failure modes in DP vessel operations over the 2015-2024 period by integrating a structured literature review, bibliometric co-occurrence analysis, and content analysis of incident and reliability studies. A PRISMA-style screening applied to Scopus and complementary databases identifies 26 relevant publications, of which 10 are selected as core analytical studies addressing DP failure mechanisms and reliability modelling. The synthesis indicates that technical failures in power generation and distribution, thruster and propulsion systems, and sensor and reference subsystems dominate DP incidents in drilling and construction operations. In addition, human and organisational factors are directly involved in approximately 20% of reported incidents based on a dataset of 311 DP cases, with higher proportions (up to 29.5%) observed in drilling and diving operations where human involvement is more direct. The variation across studies reflects differences in operational context, incident classification methodology, and dataset scope. Incident-based risk analyses further show that power generation failures and adverse environmental conditions disproportionately contribute to expected economic losses. Quantitative reliability and RAM studies consistently report lower failure probabilities for DP3 architectures compared with DP2, while identifying components such as busbars and wind sensors as critical risk contributors. Recent advances, including Bayesian networks, Monte Carlo-based RAM modelling, and the Dynamic Positioning Reliability Index (DP-RI), as well as LSTM-based real-time reliability prediction, demonstrate the potential of data-driven methods to combine incident statistics, equipment failure data, and operational conditions into dynamic risk indicators. Building on these insights, this paper proposes a hybrid framework integrating incident analytics, RAM modelling, and AI-enabled condition monitoring to support more resilient DP operations and to inform future research on human reliability, predictive maintenance, and decision-support integration with class and industry guidance.