# Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
## 3 Algorithm  
Measured acceleration was analysed by computing vibration-based fingerprints for each sensor position along the rotor blade. In the following, vibrations are classified as Type 1 and Type 2 vibrations:  
1. Vibrations with small bandwidths and small variation with environmental or operational parameters due to changes in stiffness of the blade. This applies to eigenfrequencies as characterised in [5,6] but also to increased noise levels in narrow-band frequencies.
2. Vibrations with large bandwidths and large variation with environmental or operational parameters. For example, vibrations due to fluttering of the blade tip may increase with the rotation frequency.  
As a general remark, amplitudes of blade eigenfrequencies are usually small since blade designers try to avoid any excitation of eigenfrequencies along the blade. However, by considering varying environmental conditions and using signal processing methods, those frequencies can also be extracted. Both Type 1 and Type 2 vibrations may correspond to local or global properties of the blade, i.e., may be detected across multiple positions along the blade or only at a selected position. In our analysis, vibration-based fingerprints were created separately for each sensor and, hence, Type 1 and Type 2 vibrations were primarily distinguished by their variability and bandwidth.  
The proposed analysis method consists of six steps, as visualised in Figure 4. First, frequency spectra were computed and resulting spectra were averaged by converting unscheduled measurement campaigns to a reference grid, e.g., the rotation frequency (_Spectrum Averaging_). Second, dominant frequencies called _peaks_ were identified in computed frequency spectra (_Peak Detection_). Next, Type 1 vibrations were detected by means of those peaks, i.e., vibrations with small variation with the reference variable (_Type 1 Detection_). Then, _Peak Denoising_ was conducted and least prominent peaks corresponding to noise were discarded. Last, Type 2 vibrations with large variation with the reference variable were identified by using the remaining set of peaks (_Type 2 Detection_). Detected Type 1 and Type 2 vibrations were then stored for subsequent analysis. The results were visualised separately or jointly for selected sensors (_Export and Visualisation_).  
The proposed fingerprint algorithm can be applied to vibration measurements in many structural health monitoring applications by adjusting parameters to the properties of measurements. In the subsequent analysis, parameters were empirically selected for acceleration measurements on wind turbine blades. Since vibration measurements collected on Test Turbines 1 and 2 differed with respect to sample size and measurement  
Figure 3: (**Left**) Sensor positions of Sensors \(S1\) and \(S2\) on Test Turbine 1. (**Right**) Sensor positions of Sensors \(S3\)–\(S32\) on Test Turbine 2. Positions are marked with red circles and blue rectangles for positions on pressure side and suction side, respectively. Marker faces are solid for sensors deployed on all three blades and blank for sensors only deployed on the first blade (Blade \(A\)). Allocation of sensor numbers and positions displayed with results in the remainder of this paper.  
noise, parameters were adapted separately for each turbine and are specified along with corresponding results in Section 4.