## 5 Conclusions  
Wind turbines are critical infrastructure, requiring effective monitoring to ensure structural integrity. This integrity is essential in the context of global climate policy goals, particularly those pursued in Germany, and to ensure the security of supply. To address this, acoustic methods for monitoring damaging events in tower structures were evaluated, primarily focusing on the highly stressed tendons. A monitoring concept based on stochastic models to calculate the probability of detection of wire breaks for individual signal features, aiming for a 90% detection probability at a 95% confidence level. Key findings include the following:  
* Operational noise was analyzed over a period of approximately one year, revealing no time-dependent behavior.
* Noise levels at the transition piece (upper anchor of the tendon) were 16 dB higher than at the foundation, primarily due to closer proximity to the nacelle, respectively greater distance at the lower anchor with acoustic shielding provided by the concrete tower.
* Higher wind and rotor speeds and smaller blade pitch angles increased noise levels. However, the isolated identification of the individual influences of rotor speed, blade pitch angle, and wind speed proved practically unfeasible.
* Tendon monitoring is feasible over 17.2 m, requiring five sensors per 80 m tendon. However, focusing on anchorages is more practical.
* High impedance in novel anchorage requires individual sensors (24 total), while conventional anchorage allows grouped monitoring, reducing sensor needs to seven at the foundation. At the transition piece, grouped monitoring is likewise possible, making five sensors necessary.  
This study establishes a baseline for a generalized monitoring concept to detect wire breaks in tendons of hybrid towers. The feasibility of AE for this application has been demonstrated. However, further research is required to assess the transferability of the findings to towers of the same and different construction types, as the present study was limited to a single monitored tower. Additionally, the potential for optimization through multi-criteria models and advanced signal processing was not explored. Future work should enhance detection accuracy and minimize sensor requirements, particularly in noisy environments, such as [45].  
Figure 14: Top view of sensor positions at the entrance level for conventional and novel anchorage (**left** and **center**) and the transition piece (**right**).  
**Author Contributions:** Conceptualization, M.F., R.X. and S.M.; methodology, M.F.; software, M.F. and R.X.; validation, M.F. and R.X.; formal analysis, M.F.; investigation, M.F.; resources, M.F.; data curation, M.F.; writing--original draft preparation, M.F. and R.X.; writing--review and editing, M.F., R.X., A.L., S.M. and J.O.; visualization, M.F.; supervision, S.M., J.O. and T.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This paper presents results of the research project KISWind (AI-supported acoustic emission monitoring for automatic damage detection in support structures of wind turbines) funded by the Federal Ministry for Economic Affairs and Climate Action, Germany (funding reference: 03 EE2025C).  
**Institutional Review Board Statement:** Not applicable  
**Informed Consent Statement:** Not applicable  
**Data Availability Statement:** Data will be made available on request.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Marx Krontal Partner (MKP GmbH). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.