Institutional Repository
Technical University of Crete
Technical University of Crete
EN
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EL
| URI: | http://purl.tuc.gr/dl/dias/F3FB7969-74D0-467D-92BA-E95DFA0298E2 | ||
| Year | 2019 | ||
| Type of Item | Diploma Work | ||
| License |
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| Bibliographic Citation | Emmanouil Paterakis, "Structural health monitoring of a wind turbine wing using neural networks", Diploma Work, School of Production Engineering and Management, Technical University of Crete, Chania, Greece, 2019 https://doi.org/10.26233/heallink.tuc.82631 | ||
| Appears in Collections |
Due to the stochastic nature of environmental loadings, a lot of interest is paid in the discovery of possible damages of the involved equipment in modern industry. In wind turbine's blades, where access is difficult and expensive, the development of a smart structural health monitoring system is essential. In the present paper, a large-scale composite wind turbine blade model is designed and used for the detection of several damage scenarios. The process which is presented here is mainly based on the development of monitoring techniques which exploit the capabilities of artificial neural networks. These techniques can provide the exact position of possible damages, under given external loading scenarios. Moreover, the use of such methods decreases significantly the need of external intervention and at the same time it increases the accuracy of the whole approach. The above processes are simulated using the finite element method.
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