TY - JOUR ID - 3858 TI - Model-based Data-driven Structural Health Monitoring of a Wind Turbine Blade JO - AUT Journal of Mechanical Engineering JA - AJME LA - en SN - 2588-2937 AU - Emami, Alireza AU - Tavana, Alireza AU - Mahnama, Maryam AU - Sadighi, Ali AD - Institute for Structural Lightweight Design, Johannes Keppler University, Linz, Austria AD - Faculty of Civil Engineering, University of Tokyo, Tokyo, Japan AD - School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran Y1 - 2021 PY - 2021 VL - 5 IS - 1 SP - 97 EP - 108 KW - Structural health monitoring KW - Wind turbine blade KW - Finite element modelling KW - Feature Extraction KW - One-Class Kernel SVM DO - 10.22060/ajme.2020.17245.5853 N2 - The development of structural health monitoring algorithms for wind turbines is an emerging need because of the aging issue in wind farm facilities. An emerging field of data-driven machine learning schemes has resulted in the development of new means in structural health monitoring. Although, these approaches are inclined to errors in the absence of good insight into the physics of the system. Therefore, a comprehensive model of the structure, as well as its uncertainties, could be a good complement 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 with 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 is trained to find the data associated with a damaged state of the structure. Finally, the success of the procedure in the detection of the existence and location of the damage is depicted. UR - https://ajme.aut.ac.ir/article_3858.html L1 - https://ajme.aut.ac.ir/article_3858_8b113911091cdb36eb8b2d6af9ea5977.pdf ER -