Data-driven predictive maintenance for two-stroke marine diesel engines using machine learning and MLOps

Digital ship operation is just around the corners with the rapid development of Artificial Intelligence (AI) and Industrial Internet of Things (IIoT) technologies. Real time condition monitoring and Predictive Maintenance (PdM) of marine diesel engines are crucial to realize the success of ship digital operations. The study investigates the PdM in two-stroke marine diesel engines using Machine Learning (ML) and Machine Learning Operations (MLOps) based on engine operational data. Practical data with labeled engine scuffing incidents are collected from a shipping company. The real scuffing incidents are predicted based on the expected operational behavior modeling method and a customized framework. Three case studies are conducted based on 2 different vessels for the purpose of model validations and further investigation. During the expected behavior modeling procedure, comparisons among different ML models accounting for various parameters (E.g., targets, operational features, moving average types and widths) are conducted and sensitivity studies are performed in order to identify the best solutions for engine PdM in shipping practice. Based on the study, the model effectiveness and efficiency are demonstrated and a limited generalization ability of the expected behavior modeling method with ML has been realized, which can facilitate the alarming and scheduling of maintenance events for vessels. The models and findings from this research work can be easily adapted for possible future use in ship operations. • A framework to do PdM for marine engines with application procedure is developed. • MLOps tools significantly accelerate the data-driven modeling process in engine PdM. • Data-driven methods with MLOps can potentially bridge skills gap in engine PdM. • A data-driven model with temperature as target leads to more effective detection. • Generalization ability of PdM models can be improved across vessels with MLOPs.