Introduction
The maritime defence sector faces growing challenges in achieving sustainability and operational efficiency, particularly in integration testing for new technologies onboard future ships.
Maritime integration testing verifies that various ship or offshore platform components function together as intended, ensuring functionality, performance, reliability, compatibility, and safety under different scenarios. Energy security, climate resilience, and emissions reduction are increasingly critical concerns (A. Gonzalez. et al. 2008). Artificial Intelligence (AI) presents a promising avenue for addressing these challenges by improving energy management, optimizing test simulations, and automating analysis to enhance sustainability and efficiency (Munim, Z. H. et al. 2020).
Research Problem and Questions
This project investigates how AI can enhance maritime integration testing by addressing key sustainability and efficiency concerns. The core research problem is: How can AI-driven approaches improve energy security, climate resilience, and emissions reduction while optimizing the efficiency of maritime integration testing?
Methodology
The research will employ a combination of AI methodologies, data analytics, and simulation-based approaches.
The methodology includes:
Machine Learning & Predictive Analytics: AI algorithms will be used to model energy consumption and predict system performance, helping to improve energy efficiency and security (Gavin Yeo, et al. 2019).
Reinforcement Learning: Optimization techniques will be applied to enhance the efficiency of test procedures, reducing the resource intensity of simulations.
AI-driven Climate Risk Modeling: Machine learning models will assess climate resilience by analyzing historical and real-time environmental data.
Data Mining & Automated Analysis: AI will be leveraged to enhance emissions monitoring, optimize sustainability strategies, and support decision-making processes in maritime testing (Maria Fleischer Fauske, et al. 2019).
Data-driven Simulation: Virtual models of integration testing environments will be developed to evaluate AI-driven improvements before real-world implementation (Christoph H. Loch, et al. 2001).
References:
A. Gonzalez, E. Piel, H. -G. Gross and M. Glandrup, "Testing Challenges of Maritime Safety and Security Systems-of-Systems," Testing: Academic & Industrial Conference - Practice and Research Techniques (taic part 2008), Windsor, UK, 2008, pp. 35-39, https://doi.org/ 10.1109/TAIC-PART.2008.14
Munim, Z. H., Dushenko, M., Jimenez, V . J., Shakil, M. H., & Imset, M. (2020). Big data and artificial intelligence in the maritime industry: a bibliometric review and future research directions. Maritime Policy & Management, 47(5), 577–597. https://doi.org/10.1080/03088839.2020.1788731
Gavin Yeo, Shiau Hong Lim, Laura Wynter, Hifaz Hassan (2019) MPA-IBM Project SAFER: Sense-Making Analytics for Maritime Event Recognition. INFORMS Journal on Applied Analytics 49(4):269-280. https://doi.org/10.1287/inte.2019.0997
Maria Fleischer Fauske, Carlo Mannino, Paolo Ventura (2019) Generalized Periodic Vehicle Routing and Maritime Surveillance. Transportation Science 54(1):164-183. https://doi.org/10.1287/trsc.2019.0899
Christoph H. Loch, Christian Terwiesch, Stefan Thomke, (2001) Parallel and Sequential Testing of Design Alternatives. Management Science 47(5):663-678. https://doi.org/10.1287/mnsc.47.5.663.10480