Data fusion-based damage identification for a monopile offshore wind turbine structure using wireless smart sensors. (1st January 2020) - Jeong, Seunghoo; Kim, Eun-Jin; Shin, Do Hyoung; Park, Jong-Woong; Sim, Sung-Han
Abstract: Wind turbines have been constructed in many offshore locations in response to the recent emphasis on the importance of renewable energy. Even though these wind structures are exposed to harsh environmental conditions such as strong winds and tides, few previous studies have focused on this issue. This study proposes a damage-identification method to detect structural damage occurring near the bottom of the monopile offshore wind structures using data fusion of acceleration and angular velocity responses. Traditional damage-detection methods have utilized a single type of measurement such as acceleration or strain; however, the proposed method is based on the combination of two different types of measurement to significantly improve the damage-detection threshold. A damage sensitivity index is proposed that is a function of the energy ratios between the acceleration and angular velocity responses at each natural mode. Both numerical simulations and laboratory-scale experiments are conducted to validate the efficacy of the proposed damage-detection method when applied to a monopile offshore wind structure. Highlights: A damage detection method using data fusion of acceleration and angular velocity is developed for monopile offshore wind turbine structures. The proposed damage detection method combines measured acceleration and angular velocity to calculate a damage-sensitive index. The proposed damage detection method is efficient in that a small number of sensors is required. The proposed damage detection method is implemented on the Imote2 smart sensor platform. The proposed method is validated numerically and experimentally.