# Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
## 6 Conclusions  
In wind energy, monitoring of structural health is essential to minimise blade loads and increase the energy capture per turbine. Blade vibrations need to be studied both at different positions along the blade and during operation of the turbine. Thereby, turbine settings can be optimised to decrease vibrations at relevant positions along the blade.  
In our approach, we measure vibrations at multiple positions along the blade by mounting wireless sensors on the blade surface. We propose an algorithm for automaticallyextracting the vibrational characteristic of the blade from acceleration measurements. The algorithm was tested on two test turbines, and the results demonstrate that the algorithm successfully extracts eigenfrequencies, narrowband noise and vibrations. In addition, the results indicate different vibrational characteristics of the blade at different sensor positions but will need to be verified with further measurements. Overall, the advantages of the vibration-based fingerprint algorithm can be characterised as follows:  
* Automated extraction of dominant vibrations (e.g., eigenfrequencies or narrow-band noise) from measured frequency spectra.
* Analysis with regard to operational or environmental conditions.
* Continuous monitoring to track changes in the vibrational characteristic during the lifetime of the blade.
* Comparison of fingerprints for several positions along the blade, e.g., to optimise sensor positions in further monitoring applications or to improve blade design.  
These benefits were demonstrated by means of acceleration measurements on wind turbine blades. In addition, the algorithm is promising for additional sensor types and structural health monitoring applications, in which vibrations have to be analysed with regard to varying environmental or operational conditions. For this, algorithm parameters can easily be adapted to the properties of measurements. With respect to wind energy, our algorithm contributes to gain a detailed understanding of blade vibrations during operation of the turbine. By incorporating this knowledge into operational turbine settings for existing turbines and blade design for next-generation turbines, blade vibrations can be minimised, thereby reducing wear of the blades and preventing preliminary aging.  
In future work, it is planned to mount sensors on further test turbines and analyse vibration-based fingerprints across various turbine types. In addition, the fingerprint algorithm shall be extended by alarm thresholds in case of non-optimal turbine settings.  
Conceptualisation, T.L. and A.B.; methodology, formal analysis, data curation and software, T.L.; validation, T.L. and A.B.; writing--original draft preparation, T.L.; writing--review and editing, T.L. and A.B.; visualisation, T.L.; supervision, A.B.; All authors have read and agreed to the published version of the manuscript.  
The authors received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Open Access Publication supported by the Open Access Publishing Fund of Graz University of Technology. The authors would like to thank the company eologix sensor technology for providing sensor data and equipment, and the company VERBUND for supporting sensor mounting on their turbine.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
A1 Artificial intelligence  
DC Direct current  
DFT Discrete Fourier transform  
MEMS Micro-electro-mechanical system  
SSE Sum of squared errors  
STFT Short-time Fourier transform  
UAV Semi-autonomous unmanned aerial vehicle