Abstract Summary
The determination of Value of Information (VOI) of Structural Health Monitoring systems using Bayesian Pre-posterior analysis provides the optimal time and costs for the adoption of monitoring systems over the service life of an Offshore Wind Turbine (OWT). Previous research by the authors dealt with the development of a decision-making model through the estimation of VOI through the simulations of load responses on a 5-MW OWT. In this model, the prior probabilities of failure are derived from extrapolating the load simulations to model deterioration of OWT structure. The Bayesian Pre-posterior analysis is applied by updating the prior probabilities with the updated posterior probabilities by considering the probability of detection attributes of the monitoring system. The VOI is then determined through the rollback technique of expected costs of prior and posterior information utilising the decision trees for visualisation. This paper aims to compare the VOI analysis obtained through simulations of dynamic responses, laboratory, and field data of wind turbine structures conducted by researchers in the past. The laboratory test results have determined the fatigue crack growth rates for offshore wind turbine foundation structures such as monopiles. These parameters will be evaluated and compared with the load simulations in the developed model. The effects of variation of these parameters on the VOI will be presented. The field data comprises of long-term monitoring sensor data on an onshore wind turbine structure. The deterministic and probabilistic fatigue analysis will be presented by estimating the long-term probability distribution functions for stress characteristics on the structure. These evaluated parameters further will be used to calculate the VOI and compared with offshore and onshore characteristics.
