A Digital Twin Framework for Monitoring and Fault Identification of Marine Internal Combustion Engines

Monitoring the current technical condition of a marine internal combustion engine and diagnosing the causes of deviations of its operating parameters from reference values are important tasks for ensuring the efficient and safe operation of marine power plants and vessels in general. This study develops a digital twin framework for marine engine diagnostics and demonstrates its integration with the DEPAS 5.0W diagnostic system. The framework combines a calibrated 0D–1D engine-process model, sensor-based identification of the current operating mode, reference-state generation, and inverse identification of physical factors responsible for deviations from expected engine behavior. The mathematical model is adapted for digital twin operation through operating-mode-dependent parameterization, including a modified fuel-injection approximation. A four-factor constrained factorial numerical experiment with second-order response-surface models is used to investigate the inverse diagnostic problem. The approach is demonstrated first using virtual marine engines and subsequently using experimentally acquired data from a Weichai WP4 diesel generator with a controlled intake restriction introduced by a throttle valve. The results demonstrate that the proposed framework supports the identification of the most probable physical cause of the observed deviation when multiple potential fault factors are considered. At the same time, the results reveal the practical non-uniqueness of the inverse problem, as different factor combinations may produce similar observable engine responses. The Mahalanobis distance is shown to provide improved fault discrimination in the investigated case compared with individual pressure-based objective functions. The proposed architecture provides a basis for further development of automated digital twin-assisted diagnostics, including systematic uncertainty and identifiability analysis and validation under controlled faults.