Towards a digital twin approach for vessel-specific fatigue damage monitoring and prognosis

This paper proposes a Digital Twin approach for the monitoring and prognosis of vessel-specific fatigue damage. During design, fatigue damage estimates are based on conservative assumptions regarding operational conditions and structural response. However, variability in the vessel-specific operations from those assumed during design needs to be considered when supporting engineering-based decisions for maintenance deferrals and service life extensions. The use of Digital Twins is proposed to provide this necessary vessel-specific decision support. Digital Twins typically rely on sensor-based data to update their models, however structural health sensors for fatigue monitoring can be prohibitively expensive to install and maintain in ship structures, so the proposed method addresses this by instead combining publicly available vessel-specific operational data (global vessel position data coupled with metocean hindcast data) with computational models to monitor the environmental exposure and track the vessel fatigue accumulation over time. This approach is demonstrated through a case study of a containership that has been in operation for seven years. The results of proposed approach are compared against the fatigue estimate obtained using the design reference wave conditions. The Digital Twin is then used to forecast the remaining fatigue life, in order to support inspection and maintenance scheduling and operational decision-making.