A framework to assure the trustworthiness of physical model-based digital twins for marine engines

Digital twins (DTs) are gradually employed in the maritime industry to represent the physical systems and generate datasets, among others. However, the trustworthiness of both the digital twins and datasets must be assured. This study aims at developing a framework to assure the trustworthiness of marine engines DTs based on first-principle models. This framework considers the phases of the DT development, progressivity, and trustworthiness assurance, the latter being based on three steps, namely validation, verification, and robustness. Subsequently, a methodology is applied to develop the DT of a marine engine for healthy conditions, which is extended to represent a wider operating envelope considering systematically identified anomalies. The results demonstrate that the developed DT trustworthiness is assured, as the validation step provided errors within ±3%, the verification step provided sound trade-offs, whereas the robustness assessment step confirmed acceptable uncertainty ratios. Subsequently, the DT is employed to generate datasets required for developing a data-driven model for anomaly diagnosis, which exhibits an accuracy of 98.8% for anomaly detection, 97.6% for anomaly identification, and 90.1–91.8% for anomaly isolation. This is the first study addressing the trustworthiness of DTs for marine engines, and as such advances concepts of the fourth industrial revolution to the shipping industry.