A REVIEW OF FAILURE MODES IN DYNAMIC POSITIONING VESSEL OPERATIONS
Abstract
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.
References
Mehrzadi, M., Terriche, Y., Su, C.-L., Bin Othman, M., Vasquez, J. C., & Guerrero, J. M. (2020). Review of dynamic positioning control in maritime microgrid systems. Energies, 13(12), 3188. https://doi.org/10.3390/en13123188
Kvaal, S., Østby, P., & Breivik, M. (2022). DP and the art of perfect positioning [White paper]. Kongsberg Maritime.
Fernandez, C., Kumar, S. B., Woo, W. L., Norman, R., & Dev, A. K. (2018). Dynamic positioning reliability index (DP-RI) and offline forecasting of DP-RI during complex marine operations. In Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering (Vol. 51203, p. V001T01A061). ASME.
Dong, Y., Rokseth, B., Vinnem, J. E., & Utne, I. B. (2017). Analysis of dynamic positioning system accidents and incidents with emphasis on root causes and barrier failures. In Risk, reliability and safety: Innovating theory and practice (p. 29). CRC Press.
Wang, F., Zhao, L., & Bai, Y. (2024). Survey on reliability analysis of dynamic positioning systems. Ships and Offshore Structures, 19(8), 999–1009. https://doi.org/10.1080/17445302.2023.2241641
Dong, Y., Vinnem, J. E., & Utne, I. B. (2017). Improving safety of DP operations: Learning from accidents and incidents during offshore loading operations. EURO Journal on Decision Processes, 5(1–4), 5–40.
Olubitan, O., Loughney, S., Wang, J., & Bell, R. (2018). An investigation and statistical analysis into the incidents and failures associated with dynamic positioning systems. In Safety and reliability – Safe societies in a changing world (pp. 79–85). CRC Press.
Clavijo, M. V., Martins, M. R., & Schleder, A. M. (2018). Reliability analysis of dynamic positioning systems. In Progress in maritime technology and engineering (pp. 265–272). CRC Press.
Clavijo, M. V., Schleder, A. M., Droguett, E. L., & Martins, M. R. (2022). RAM analysis of dynamic positioning system: An approach taking into account uncertainties and criticality equipment ratings. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 236(6), 1104–1134.
Varela, Z. S., Boullosa-Falces, D., Boko, Z., & Skoko, I. (2022). Human error analysis in dynamic positioning incidents according to the nature of the operations in progress. Paper presented at the Maritime Transport Conference (MTC 2022), Barcelona, Spain.
Sanchez-Varela, Z., Boullosa-Falces, D., Skoko, I., & Boko, Z. (2024). Analysis of human-related incidents during dynamic positioning operations. Journal of Marine Science and Engineering, 12(6), 907.
Huang, X., & Chen, J. (2015). Time-dependent reliability model of deteriorating structures based on stochastic processes and Bayesian inference methods. Journal of Engineering Mechanics, 141(3), 04014123.
Sanchez-Varela, Z., Boullosa-Falces, D., Larrabe-Barrena, J. L., & Gomez-Solaeche, M. A. (2021). Risk analysis of DP incidents during drilling operations. Transactions on Maritime Science, 10(1), 84–100.
Fernandez, C., Kumar, S. B., Woo, W. L., Norman, R., & Dev, A. K. (2020). Real-time prediction of reliability of dynamic positioning sub-systems for computation of dynamic positioning reliability index (DP-RI) using long short-term memory (LSTM). In Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering. ASME.
Settemsdal, S., & Radan, D. (2012). DP3 class power system solution for dynamically positioned vessels. Paper presented at the Dynamic Positioning Conference, Houston, TX.
Tuo, Y., Lin, J., Peng, Z., Wang, Y., & Wang, S. (2025). An energy-efficient thrust allocation based on the improved dung beetle optimizer for the dynamic positioning system of vessels. Journal of Marine Science and Engineering, 13(6), 1041.
Gao, X., & Li, T. (2024). Dynamic positioning control for marine crafts: A survey and recent advances. Journal of Marine Science and Engineering, 12(3), 362. https://doi.org/10.3390/jmse12030362
Karkori, F. (2024). Dynamic positioning systems: Class guidance for DP operators (Vol. 21). Springer Nature.
Chae, C.-J. (2025). The evolution of maritime technology development: A dynamic positioning system perspective of maritime autonomous surface ships. WMU Journal of Maritime Affairs, 24(1), 99–127.
Marine Technology Society (MTS) Dynamic Positioning Committee. (2021). DP vessel design philosophy guidelines (Rev. 2-Apr ed.). https://dynamic-positioning.com/files_mailing/MTS%20DP%20VESSEL%20DESIGN%20PHILOSOPHY%20GUIDELINES%20(Rev2%20-%20Apr21).pdf
Snyder, J. (n.d.). Data on DP incidents suggests industry ‘becoming less safe’. Riviera Maritime Media. https://www.rivieramm.com/news-content-hub/news-content-hub/data-on-dp-incidents-suggests-industry-becoming-less-safe-72987
Vedachalam, N., & Ramadass, G. A. (2017). Reliability assessment of multi-megawatt capacity offshore dynamic positioning systems. Applied Ocean Research, 63, 251–261. https://doi.org/10.1016/j.apor.2017.02.001
Javanmardi, A., & Ghobad, M. (2025). Research of critical causes of solar panel aging based on fuzzy fault tree and Pareto chart approach. Journal of Applied Engineering Science, 23(2). https://doi.org/10.5937/jaes0-56142
Simion, D., Postolache, F., Fleacă, B., & Fleacă, E. (2024). AI-driven predictive maintenance in modern maritime transport—Enhancing operational efficiency and reliability. Applied Sciences, 14(20), 9439.
Albuquerque, C. X. A., Lima, S., Carvalho, G., Leite, F., & Machado, R. (n.d.). Data analytics, artificial intelligence facilitate development of failure prediction models for subsea BOPs. Drilling Contractor. https://drillingcontractor.org/data-analytics-artificial-intelligence-facilitate-development-of-failure-prediction-models-for-subsea-bops-57655
Li, J., Xiang, X., & Yang, S. (2022). Robust adaptive neural network control for dynamic positioning of marine vessels with prescribed performance under model uncertainties and input saturation. Neurocomputing, 484, 1–12.
Pavlov, A. N., Pavlov, D. A., Kulakov, A. Y., & Zakharov, V. V. (2024). Study of technology for the reliability and survivability modelling of onboard control system of small spacecraft operating in complex modes. Journal of Applied Engineering Science, 22(3), 612–620. https://doi.org/10.5937/jaes0-50149
