Development of a remaining useful life prediction model for marine diesel engine filtration systems

Abstract

Marine diesel propulsion engines are essential to naval platforms, enabling maneuvering, navigation readiness, and training operations. However, maintenance of propulsion consumables—particularly fuel filtration elements—often remains time-based and corrective despite the growing availability of onboard operational records. This paper presents the development and validation of a Remaining Useful Life (RUL) prediction model for the propulsion engine filtration system of the Colombian Navy (ARC) training ship, aiming to estimate time to replacement for cartridge-based filters. The proposed approach handles imperfect manual operational data and scarce, non-uniform maintenance labels through a Prognostics and Health Management (PHM) workflow guided by the Cross-Industry Standard Process for Data Mining (CRISP-DM). It combines physics-informed data quality control using plausibility bounds, outlier mitigation, and time-series reconstruction; expert validation of representative operating cycles using a Delphi protocol; and event logging to align filter-replacement actions with gap-aware approximations. It was trained supervised regression models using an automated machine learning (AutoML) strategy implemented in PyCaret and refined through hyperparameter optimization in Optuna. A Random Forest model achieved the best performance, reaching a test root mean squared error (RMSE) of 52.92 hours with a coefficient of determination of 0.921.