# Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
## 4 Results
### Test Turbine 1  
For the first test turbine, the two Sensors \(S1\) and \(S2\) were mounted at the blade tip at 88% and 98% of the blade length, respectively. The fingerprint algorithm was applied to the dataset of each sensor separately and parameters were adjusted as displayed in Table 1. Since the dataset was large with 1640 and 1100 measurements for Sensors \(S1\) and \(S2\), respectively, the reference grid could be set to a high resolution with \(\Delta r=7\times 10^{-4}\). This led to a large number of averaged spectra and, thus, a low noise level, which allowed localising Type 1 vibrations with a width of \(f_{width}=1\times 10^{-3}\). In addition, parameters were set separately for the lower part (\(0\leq a_{0}<0.3\)) and the upper part (\(0.3\leq a_{0}<1\)) of the data range during the detection of Type 2 vibrations. Thereby, different properties of measurements such as well-localised and adjacent vibrations in the lower part of spectrograms and vibrations with large bandwidth in the upper part of spectrograms were considered. Algorithm results for Sensors \(S1\) and \(S2\) are presented in Figure 11. In averaged spectrograms, several distinct Type 2 vibrations are visible below 25 Hz and two Type 2 vibrations with higher bandwidth appear in the higher frequency range above 75 Hz. In addition, higher noise levels are visible for Sensor \(S2\), which was mounted further towards the tip of the blade. Extracted vibration-based fingerprints show that Type 2 vibrations were correctly identified and extracted from averaged spectrograms. In those vibrations, a larger bandwidth of vibrations reflects higher noise levels for Sensor \(S2\). Furthermore, several Type 1 vibrations width small bandwidth were identified for both sensors corresponding to either eigenfrequencies of the blade or increased narrow-band noise. These vibrations are \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Processing** & **Parameter** & **Turbine 1** & **Turbine 2** \\ \hline \multirow{4}{*}{Spectrum Averaging} & \(L_{G}\) & 163 (S2), 109 (S2) & 58 \\ \cline{2-4}  & \(L_{M}\) & 1640 (S1), 1100 (S2) & 141–170 \\ \cline{2-4}  & \(\Delta r\) & \(7\times 10^{-4}\) & \(5\times 10^{-3}\) \\ \cline{2-4}  & \(f_{rot,min}[Hz]\) & \(1\times 10^{-1}\) & \(1\times 10^{-1}\) \\ \cline{2-4}  & \(f_{rot,max}[Hz]\) & \(2.2\times 10^{-1}\) & \(3.9\times 10^{-1}\) \\ \hline Peak Detect. & \(p_{red}\) & \(3.3\times 10^{-1}\) & \(2.5\times 10^{-1}\) \\ \hline Type 1 Vib. & \(f_{width}\) & \(1\times 10^{-3}\) & \(5\times 10^{-3}\) \\ \hline Denoising & \(a_{denoise}\) & \(3.3\times 10^{-1}\) & \(2.5\times 10^{-1}\) \\ \hline \multirow{4}{*}{Type 2 Vib.} & \(a_{0,min}\) & 0 & 0 & \(1.5\times 10^{-1}\) \\ \cline{2-4}  & \(a_{0,max}\) & \(3\times 10^{-1}\) & \(5\times 10^{-1}\) & \(1.5\times 10^{-1}\) \\ \cline{2-4}  & \(\Delta a_{0}\) & \(5\times 10^{-3}\) & \(2\times 10^{-2}\) & \(1\times 10^{-2}\) \\ \cline{2-4}  & \(a_{1,min}\) & 0 & 0 & \\ \hline \multirow{4}{*}{\(a_{1,max}\)} & \(8\times 10^{-1}\) & \multicolumn{2}{c}{1.4} \\ \cline{2-4}  & \(\Delta a_{1}\) & \(1\times 10^{-2}\) & \(1\times 10^{-2}\) \\ \cline{2-4}  & \(\hat{x}\) & 100 & 46 & \\ \cline{2-4}  & \(a_{int}\) & \(4\times 10^{-1}\) & \(8\times 10^{-1}\) & \(5\times 10^{-1}\) \\ \cline{2-4}  & \(\epsilon_{1}\), \(\epsilon_{2}\) & \(2\times 10^{-2}\) & \(4\times 10^{-2}\) & \(8\times 10^{-3}\) & \(1\times 10^{-2}\) & \(2.2\times 10^{-2}\) \\ \cline{2-4}  & condition & strict & strict & strict & interval & interval \\ \hline \(N_{x,local,min}\) & 0 & \multicolumn{2}{c}{7.5 \(\times 10^{-1}\)} \\ \cline{2-4}  & \(N_{x,local,max}\) & 1 & \multicolumn{2}{c}{1} \\ \cline{2-4}  & \(M_{x,local}\) & 10 & 10 & 10 & 5 \\ \hline \(SSE_{max}\) & \(5\times 10^{-3}\) & \(2\times 10^{-2}\) & \(5\times 10^{-3}\) & \(1\times 10^{-2}\) & \(3\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table}