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Model-based Data-driven Structural Health Monitoring of a Wind Turbine Blade | ||
| AUT Journal of Mechanical Engineering | ||
| مقاله 5، دوره 5، شماره 1، خرداد 2021، صفحه 97-108 اصل مقاله (1.6 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajme.2020.17245.5853 | ||
| نویسندگان | ||
| Alireza Emami1؛ Alireza Tavana2؛ Maryam Mahnama* 3؛ Ali Sadighi3 | ||
| 1Institute for Structural Lightweight Design, Johannes Keppler University, Linz, Austria | ||
| 2Faculty of Civil Engineering, University of Tokyo, Tokyo, Japan | ||
| 3School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
| چکیده | ||
| 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. | ||
| کلیدواژه‌ها | ||
| Structural health monitoring؛ Wind turbine blade؛ Finite element modelling؛ Feature extraction؛ One-class Kernel SVM | ||
| مراجع | ||
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