Prognostic and health management (PHM) methods focus on improving the performance and reliability of systems with a high degree of complexity and criticality. These systems include engines, turbines, and robotic systems. PHM methods involve managing technical processes, such as condition monitoring, fault diagnosis, health prognosis, and maintenance decision-making. Various software and applications deal with the processes mentioned above independently. We can also observe different development levels, making connecting all of the machine’s technical processes in one health management system with the best possible output a challenging task. This study’s objective was to outline the scope of PHM methods in real-time conditions and propose new directions to develop a decision support tool for marine diesel engines. In this paper, we illustrate PHM processes and the state of the art in the marine industry for each technical process. Then, we review PHM methods and limitations for marine diesel engines. Finally, we analyze future research opportunities for the marine industry and their role in developing systems’ performance and reliability. The main added value of the research is that a research gap was found in this research field, which is that new advanced PHM methods have to be implemented for marine diesel engines. Our suggestions to improve marine diesel engines’ operation and maintenance include implementing advanced PHM methods and utilizing predictive analytics and machine learning.

Implementing adequate diagnostic strategies for marine engines offers a good possibility to focus on the early recognition of potentialproblems and prevent catastrophic and costly consequences. The successful and increasing application of diagnostic systems and devices for machines depends significantly on the precision of the diagnostic approaches. This research aims to develop an improved diagnosis expert system for determining the real-time technical condition of marine diesel engines. The adequate selection of the diagnostic parameters is crucial in detecting early-stage defects and failures since each parameter responds to the changes in structural parameters of the engine in different modes and degrees. This result provides valuable information on the accurate location of the fault and describes the relationship between operational and structural parameters. Firstly, this article introduces the relevant literature relating to the selection of the diagnostic parameters for marine engines and their subsystems through different statistical methods. The next part contains the description of marine diesel engine subsystems and the selected marine diesel engine’s diagnostic parameters. After that, a newly developed expert system for diagnosing marine diesel engines’ technical conditions is introduced. Finally, a case study of the operation of the developed diagnostic system is presented. The main contribution of the article is the introduction of the newly developed diagnosis expert system, which can be offered to inexperienced users on ships to effectively manage abnormal situations. Furthermore, this diagnostic tool can be applied to the engines’ subsystems to improve the reliability and efficiency of the marine diesel engines’ operation and maintenance.

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