Structural Artificial Intelligence Health Monitoring System - SAIHMS
Examensarbete för masterexamen
OLMEDO ÁVILA, ANTONIO
This Master thesis has the purpose to perform a study of the possibilities and methods applicable to the development of a health monitoring system in an innovative new type of floating offshore wind turbine platform. The lack of knowledge of the behaviour in service of elements not used before in this specific application, like sheaves and dynamic mooring lines, makes necessary the use of monitoring systems during the operation of the reduced scale prototype. Lately the use of Artificial Intelligence methods is being developed to process the big amount of data produced during the constant monitoring process. The good results obtained in the combination of monitoring services with Artificial Intelligence make those systems very attractive to possible investor in the offshore field that see their maintenance and inspections costs reduced thanks to the possibility to apply preventive maintenance before severe damage appear in the structure. The simple Neural Network architecture developed in this study proves the accuracy and efficiency of the Artificial Intelligence techniques in the structural state classification that see its efficiency improved by its combination with Machine Learning metaheuristic algorithms during its training process.
ds: offshore, offshore wind, offshore platform, floating structures, structural health monitoring , neural network, artificial intelligence, biogeography based optimization, machine learning, damage detection