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journalArticle Qiu Binbin Lu Yang Sun Liping Qu Xianqiang Xue Yanzhuo Tong Fushan ANN Damage prediction Generalized regression neural network Genetic algorithm Offshore wind turbine Research on the damage prediction method of offshore wind turbine tower structure based on improved neural network Tower structure is a basic part of offshore wind power system, and how to ensure the stability and safety of tower structure in wind turbine operation and develop the efficient and accurate damage prediction method to judge the damage location and degree of tower structure has become an important research topic in the offshore wind power industry. With high adaptability, nonlinearity and strong function approximation ability, Artificial neural network (ANN) has been widely used in structural damage prediction and showed better performance than other diagnostic methods, however, traditional Back Propagation neural network (BPNN) has shortcomings of complex training parameters and low accuracy. To address this issue, this paper considered an improved neural network damage prediction method based on step-by-step identification for offshore wind turbine tower structure, an experimental research was carried out to simplify wind turbine model and experimentally measure the intact steel cylinders with different damage locations and different damage degrees. Then, the modal analysis was carried out by using the numerical analysis method and the results were compared with those of experimental measurement to modify the model. The training data are generated by Abaqus software to verify the proposed method. Results showed that the step-by-step prediction method could effectively reduce the complexity of the network, improve the prediction accuracy of the neural network, and save the training time. 107141 October 14, 2019 en http://www.sciencedirect.com/science/article/pii/S0263224119310073 2019-10-25 07:30:51 ScienceDirect Measurement Measurement DOI 10.1016/j.measurement.2019.107141 ISSN 0263-2241