AI-Driven Predictive Maintenance in Modern Maritime Transport--Enhancing Operational Efficiency and Reliability  
[PERSON]  
1 Faculty of Entrepreneurship, Business Engineering and Management, National University of Science and Technology Politechnica Bucharest, Spliaul Independentnei 313, 060042 Bucuresti, Romania; [EMAIL_ADDRESS] (B.F.); [EMAIL_ADDRESS] (E.F.)  
[PERSON]  
2 Faculty of Marine Engineering, Romanian Naval Academy \"[PERSON]\", Fulgerluui 1, 900218 Constanta, Romania; [EMAIL_ADDRESS]  
[PERSON]  
1 Faculty of Entrepreneurship, Business Engineering and Management, National University of Science and Technology Politechnica Bucharest, Spliaul Independentnei 313, 060042 Bucuresti, Romania; [EMAIL_ADDRESS] (B.F.); [EMAIL_ADDRESS] (E.F.)  
[PERSON]  
1 Faculty of Entrepreneurship, Business Engineering and Management, National University of Science and Technology Politechnica Bucharest, Spliaul Independentnei 313, 060042 Bucuresti, Romania; [EMAIL_ADDRESS] (B.F.)  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/pdf/10/1](https://www.mdpi.com/journals/pdf/10/1)]([https://www.mdpi.com/journals/pdf/10/1](https://www.mdpi.com/journals/pdf/10/1))  
the Bay of Biscay, where a significant portion of naval incidents occur, could benefit from improved fault diagnosis systems to ensure safer and more reliable operations [1]. Artificial Intelligence (AI) and Machine Learning (ML) have become indispensable in improving predictive maintenance. Traditional maintenance strategies, such as corrective and preventive maintenance, are increasingly insufficient to meet the stringent safety and efficiency standards required by the maritime industry. AI-based learning systems, which simulate the state of industrial processes or components using available measurement data, are widely adopted in maintenance engineering. These systems help increase equipment availability, reduce maintenance costs and enhance system reliability by predicting potential failures before they occur [2]. ML, in particular, plays a critical role in analyzing large volumes of multidimensional data, enabling operators to predict system failures and estimate Remaining Useful Life (RUL) [3]. By learning from historical operational data, ML applications can identify patterns and trends that signal potential equipment failures. This is especially important in maritime operations, where incidents often result from a chain of risky events. By applying ML techniques, maintenance teams can detect faults more accurately and quickly, improving overall system safety and operational efficiency. Prognostics and Health Management (PHM), also known as Condition-Based Maintenance (CBM), addresses the limitations of traditional maintenance strategies [4]. PHM focuses on predicting and diagnosing system failures to optimize maintenance programs. It includes three main approaches: model-based, data-driven and hybrid methods [5]. Model-based approaches are highly accurate when system degradation is well-understood but require specialized expertise. Data-driven approaches, on the other hand, rely on large datasets and are more practical as they demand less specialized knowledge. Hybrid approaches combine the strengths of both methods to provide a comprehensive solution for predictive maintenance. By integrating reliability and risk analysis methods with ML, we propose a novel algorithm for enhancing equipment maintenance activities. The proposed approach monitors the functional status of naval systems and detects early signs of failure, allowing maintenance teams to act before failures become safety risks. This ML-driven maintenance solution is particularly effective in maritime operations, where the complexity of the systems and the environment requires advanced predictive capabilities. The use of AI in maintenance seeks to replicate human reasoning and decision-making processes but with greater efficiency.