# Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
## 4 Results
### Test Turbine 2  
For the second test turbine, Sensors \(S3\)-\(S32\) were mounted on all three blades of the turbine. For comparability, selected sensors were deployed at the same positions on all blades. Additional sensors were deployed at varying positions on the first blade (Blade \(A\)) for a closer inspection of different positions along the blade. It needs to be noted that the set of available measurements was much smaller for this turbine (9-15% with reference to sensors on Test Turbine 1) and both size of datasets and data quality (e.g., increased noise due to transmission errors) varied across sensors. Therefore, only selected sensors were considered in the subsequent evaluation.  
Due to smaller datasets, averaging of spectrograms had to be conducted with fewer measurement campaigns per grid point. Hence, averaged spectrograms of Turbine 2 included higher noise levels. Parameters were adapted as displayed in Table 1:  
* adaption of grid resolution with \(\Delta r=5\times 10^{-3}\);
* reduction of peaks to reduce noise for Type 1 detection (\(p_{red}=0.25\));
* less noise reduction to find Type 2 vibrations hidden in noise (\(a_{denoise}=0.25\));
* increased sensitivity to Type 2 vibrations hidden in noise (\(a_{int}=0.5\)); and
* increased range limits (\(SSE_{max}(x\geq 0.75)=3\times 10^{-2}\)).  
In addition, parameters were adjusted separately for three different parts of the data range, i.e., a lower part (\(0\leq a_{0}<0.15\)), a middle part (\(0.15\leq a_{0}<0.25\)) and an upper part (\(0.25\leq a_{0}<0.5\)). The _interval_ condition was used for the middle and upper part to increase sensitivity to those vibrations, which did not occur for the full operational range of the turbine.  
Figure 13 displays exemplary fingerprints for Sensors \(S15\) and \(S27\) mounted on tip and root of a rotor blade on Test Turbine 2. The effects of the smaller datasets are reflected in: (i) white gaps in averaged spectrograms representing missing measurements for respective grid points; and (ii) higher measurement noise, especially below 100 Hz. The fingerprint algorithm identified several Type 2 vibrations, which correspond to those vibrations clearly visible in averaged spectrograms. By using the _interval_ condition for detection, vibrations only occurring for parts of the operational range could be identified, e.g., see Sensor \(S27\) with Vibrations 2 and 4 from the top.  
Due to increased measurement noise, only Type 1 vibrations with large bandwidths were detected. For frequencies below 75 Hz, those frequencies mostly overlapped with Type 2 vibrations and, hence, decreased accuracy of Type 2 detection. Therefore, the following analysis is focused on analysing Type 2 vibrations above 75 Hz. Fingerprints for Sensors \(S15\) and \(S27\) only partly overlap for Type 2 vibrations and, hence, the properties of fingerprints of different sensors are evaluated in the following with respect to sensor positions. Due to the implications of data quality as discussed, only indications can be given in the following, and final conclusions on the vibrational characteristics of the blades, optimal sensor deployment, etc. need to be confirmed by larger datasets.