Maritime Data Analytics Using Explainable Artificial Intelligence for Energy-Efficient Shipping and Condition Monitoring of Marine Equipment

Abstract

Maritime transportation plays a vital role in global trade but faces urgent sustainability challenges related to reducing fossil fuel use and emissions. Despite being the most energy-efficient mode of transportation, maritime activities still contribute a significant amount of greenhouse gas (GHG) emissions worldwide due to their heavy reliance on oil as fuel, pressing regulatory bodies to curb this reliance. The urgency has advanced condition monitoring techniques that enable monitoring of energy efficiency and data collection for further improvements to support data-driven decision-making. The existence of big data in maritime operations, supported by technological advancements, has made this possible. While this data now facilitates more sophisticated modeling, challenges remain around fully leveraging insights and enhancing the interpretability of results. This dissertation therefore explores applying Explainable Artificial Intelligence (XAI) techniques to improve energy performance through optimized operations, predictive maintenance capabilities, and generating transparent explanations from complex algorithms used for analytics. Three major maritime tasks are explored to contribute to the industry's goals of improved energy efficiency and condition monitoring: anomaly detection modeling for vessel main engine fault diagnosis, shaft power prediction with voyage-based feature attribution analysis, and region- specific explanation of extreme high fuel oil consumption events. The research demonstrates how XAI approaches, such as model-agnostic SHAP values and localized interpretations, enable insightful and transparent machine learning solutions. These techniques provide explainability and interpretability that have become critically important as maritime operations increasingly rely on advanced analytics and black-box modeling. By unlocking the inner workings of complex algorithms, XAI paves the way for increased trust, reliability, and deployment of artificial intelligence systems in the maritime domain. This dissertation thus illuminates pathways toward more energy-efficient shipping and predictive maintenance through profound transparency into predictive model outcomes. While the three studies leveraged explainable AI techniques to provide novel insights into maritime operations, rigorous validation of the XAI outputs was also paramount. With no standardized evaluation methods established for this emerging field, tailored statistical-based approaches were developed for each part of the research item as post-analysis supplements. Focusing on two key axioms - dissimilarity and consistency - the validation sought to independently assess whether the feature attributions from XAI models reasonably distinguished important from unimportant features and whether the robustness of the proposed validation method could withstand small perturbations to the models or data. This self-designed evaluation process represents an important contribution, addressing the lack of consensus around XAI validation. Demonstrating explanatory robustness and reliability through rigorous testing represents an important methodological contribution. This work has therefore illuminated both practical challenges within shipping and pathways toward achieving more transparent, trustworthy maritime data analytics, and substantially advances maritime analytics through both the application and development of XAI techniques and evaluation practices. The explainable frameworks and customized validation approaches established herein offer a strong foundation to facilitate continued progress in predictive maintenance, optimization, and enhanced sustainability through data-driven insights. In summary, this dissertation significantly progresses maritime data science through Explainable Artificial Intelligence (XAI).