Digital Twin–Driven AI Framework for Predictive Maintenance of Marine Diesel Engines Using Multi-Sensor IoT Data

Abstract

Marine diesel engines are critical components of ship propulsion systems, and unexpected failures can lead to operational disruptions, increased maintenance costs, and reduced reliability. Conventional maintenance approaches may not adequately capture complex degradation patterns under varying operating conditions. This study proposes a Digital Twin–driven artificial intelligence (AI) framework for predictive maintenance of marine diesel engines using multi-sensor Internet of Things (IoT) data. The framework integrates engine sensor measurements, Digital Twin-derived information, and AI-based analytics to support fault detection and remaining useful life (RUL) estimation. Sensor data are processed to obtain relevant operating features, while residuals between measured and Digital Twin-predicted variables can provide additional information about deviations from expected engine behaviour. These features are used as inputs for fault classification and RUL prediction models. Based on the reported results, the proposed integrated framework achieved a fault detection accuracy of 93.8%, compared with 89.7% for the AI-based sensor data approach and 86.4% for the baseline approach. The reported RUL prediction error decreased to 9.2 hours, compared with 14.2 hours and 18.5 hours for the respective comparison approaches. These findings suggest that combining multi-sensor IoT data with Digital Twin-derived information may improve engine condition assessment and RUL estimation. However, the reliability of these findings depends on the underlying dataset, experimental design, and consistency of the evaluation results. Further validation using real-world marine engine data and diverse operating conditions is necessary to assess the framework's generalizability and practical applicability. The proposed approach offers a potential foundation for data-driven maintenance planning and improved operational reliability in marine engineering.