## 3 Methodology and Data Preparation  
### Probability of Detection and Sensor Distances  
Basic methods of parameter-based analysis were used to evaluate the suitability of acoustic emission monitoring for wind turbine applications [12]. These methods are widely used, as their straightforward determination allows for the broad application of related concepts. Consequently, they were utilized for this research. In parameter-based analysis, the measured time-amplitude signal is characterized based on certain features. These features are used to draw conclusions about the underlying causes of signal formation [37; 38; 39]. Feature extraction significantly reduces the data volume, which is why this approach is widely used in acoustic emission analysis, especially for efficiently processing large data sets. Typical features in the time domain are peak-amplitude \(A\) and signal energy \(E\). In the frequency domain, the spectral centroid \(SC\) is one feature analyzed. In this paper, these three characteristics were evaluated, with amplitude and energy being the main criteria for the intensity analysis. To develop a sensor layout, the concept of probability of detection (PoD) in acoustic emission analysis was utilized [40; 41]. PoD is a statistical framework used to evaluate the effectiveness of non-destructive testing (NDT) methods in detecting defects or damage within a structure. It quantifies the likelihood that a given sensor configuration and signal processing approach will successfully identify a defect at varying conditions, including inspector skill, inspection technique, material properties, and environmental conditions. A PoD curve shows detection probability as a function of defect size, with key points like \(a_{50}\) (50% detection) and \(a_{90/95}\) (90% detection with 95% confidence). It is crucial in safety-critical industries like aerospace and nuclear, ensuring reliable defect detection and compliance with regulatory standards. A fundamental document is the _MIL-HDBK-1923_ handbook, which has guided the creation and application of PoD [42]. This method was adapted from other applications in AE for wind energy plants from the problem of detecting wire breaks  
Figure 6: Photos of the test frame with sensor arrangement along the tendon and on the steel plate of the transition piece crossbeam (**left**) and at the novel anchoring and the foundation crossbeam (**right**). in bridge structures [31; 43]. Figure 7 illustrates this concept in an exemplary manner for one feature. Three key input components are crucial for applying this methodology: the source signal, the specific attenuation along a structure or at discrete structure transitions, and the background noise level. The knowledge of their characteristics is achieved experimentally using extensive measurements on the test objects. The required statistical parameters are derived from the data. The mean value and standard deviation describe the feature distribution of the damage signals. The attenuation is determined by regression analysis for a set of sensors. From this, an upper quantile value is considered, which, in this case, is defined as the 90% quantile. The spatial relation is established with the attenuation coefficient, and the feature considered is set in connection with the distance between the source and the sensor. The standard error of the attenuation coefficients can be calculated using individual measurements, which allows the introduction of the 95% confidence interval. For background noise, the upper quantile value of 90% is also determined, which serves as the threshold for evaluation in the model and for calculating the probability of detection. The statistical parameters are combined in a stochastic model (PoD model) based on a normal distribution and calculate the probability of a signal feature falling below the threshold value into the background noise at the respective position. The point of evaluation and, thus, the maximum sensor distance is reached at the intersection of the 95% confidence interval and a probability of 90%.