Machine Learning Applications for a Wind Turbine Blade under Continuous Fatigue Loading

摘要: Structural health monitoring (SHM) systems will be one of the leading factors in successful establishment wind turbines energy arena. Detection damage at an early stage is a vital issue as blade failure would catastrophic result for entire turbine. In this study SHM analysis based on experimental measurements vibration analysis, extracted 9m CX-100 under fatigue loading. For machine learning techniques utilised detection turbine blades applied, like non-linear Neural Networks, including Auto-Associative Network (AANN) and Radial Basis Function (RBF) networks models.