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최정철(Choi Jungchul),손은국(Son Eunkuk),이광세(Lee Gwangse),강민상(Kang Minsang),이진재(Lee Jinjae),황성목(Hwang Sungmok),박사일(Park Sail) 한국태양에너지학회 2021 한국태양에너지학회 논문집 Vol.41 No.4
Continuous fatigue information is essential for the structural health monitoring (SHM) of wind turbines. Faults, such as sensor failure, data loss, and cable disconnection, can result in a total loss of SHM. To avoid such a malfunction, machine learning algorithms and polynomial curve fitting are suggested to predict the missing fatigue data from the otherwise known measurement data. Artificial neural networks showed the best prediction performance. Decision trees and regularized linear regression are also powerful alternatives.