A digital twin framework for predictive maintenance of marine diesel engines using vibration signature analysis and deep learning

The maritime industry requires advanced condition monitoring and predictive maintenance strategies to enhance the reliability of marine diesel engine systems. This study presents a digital twin framework that integrates vibration analysis, physics-based modeling, and deep learning for real-time fault diagnosis and remaining useful life (RUL) estimation. High-frequency vibration signals are processed and analyzed using a hybrid convolutional neural network–long short-term memory (CNN-LSTM) architecture with an attention mechanism to automatically classify engine faults and predict degradation trends. The framework combines data-driven methods with thermodynamic and structural digital twin models to improve generalization across operating conditions. Experimental validation on a medium-speed marine diesel engine under controlled fault scenarios demonstrates fault classification accuracy above 95% and remaining useful life prediction error below 8%. Early degradation signatures were detected up to 150 operating hours prior to critical failure. The proposed approach supports intelligent condition monitoring and decision-making for maintenance scheduling, reducing downtime and operational risk. This research demonstrates the effectiveness of integrating digital twin technology, vibration analysis, and CNN-LSTM deep learning models for predictive maintenance of marine diesel engines.