Model-based Data-driven Structural Health Monitoring of a Wind Turbine Blade

Development of structural health monitoring algorithms for wind turbines is an emerging need because the wind farm facilities are aging. Emerging field of data-driven machine learning schemes has resulted in development of new means in SHM. Although, these approaches are inclined to errors in the absence of a good insight into physics of the system. Therefore, a comprehensive model of the structure as well as its uncertainities could be a good complementary to these approaches. In the current article, an algorithm is developed for autonomous health monitoring of a wind turbine blade, which is one of the most expensive parts of the turbine, based on acceleration measurements taken from several points on the blade. The data are acquired based on a close to reality finite element model of the blade. The acceleration signals are gathered from five nodes along the wind turbine model, which act as vibration sensors in a common similar test setup. Advanced algorithms of system identification are used for extracting damage sensitive features. Moreover, a one-class kernel support vector machine (SVM) is trained to find the data associated with a damaged state of the structure. Finally, success of the procedure in detection of the existence and location of damage is depicted.