Alireza Emami Javid - BSc student, School of Mechanical Engineering, College of Engineering, University of Tehran, P. O. Box: ۱۱۱۵۵-۴۵۶۳, Tehran, Iran.
Alireza Tavana - BSc student, School of Mechanical Engineering, College of Engineering, University of Tehran, P. O. Box: ۱۱۱۵۵-۴۵۶۳, Tehran, Iran.
Ali Sadighi - Assistant Professor, School of Mechanical Engineering, College of Engineering, University of Tehran, P. O. Box: ۱۱۱۵۵-۴۵۶۳, Tehran, Iran.
Maryam Mahnama - Assistant Professor, School of Mechanical Engineering, College of Engineering, University of Tehran, P. O. Box: ۱۱۱۵۵-۴۵۶۳, Tehran, Iran.

Development of structural health monitoring algorithms for wind turbines is an emerging need because the wind farm facilities are aging. 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. A close to reality finite element model of the blade is used for data acquisition. Advanced algorithms of system identification are used for extracting damage sensitive features. Moreo-ver, 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 detecting the existence and location of damage is depicted.

Structural Health Monitoring; Wind Turbine Blade; Finite Element Modelling; Fea-ture Extraction; System Identification; One-Class Kernel SVM.