# Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
## 1 Introduction
### Related Literature  
Blade monitoring refers to both monitoring of vibrations, e.g., fluttering at the blade tip, and monitoring of blade bending, e.g., alternate bending in nonuniform wind profiles. Those two aspects intertwine, i.e., a reduction of blade bending and loads, reduces vibrations and vice versa. This study solely focused on monitoring blade vibrations; however, our proposed sensing principle is also suited for monitoring blade bending as studied in [4]. In general, blade vibrations refer to global properties, e.g., eigenfrequencies, and local properties, e.g., increased vibrations due to fluttering at the blade tip. Both highly depend on environmental conditions such as temperature and wind speed and operational conditions such as rotation speed. Since measuring the vibrational response is costly and technically challenging, characteristics of blade vibrations have been derived in several simulation studies [5; 6; 7; 8]. [PERSON] and [PERSON] [5] and [PERSON] et al. [6] demonstrated that eigenfrequencies depend on the stiffness of the blade and vary with operational and environmental parameters. Consequently, eigenfrequencies need to be studied under a large variety of conditions. In addition, [PERSON] and [PERSON] [7] showed that centrifugal inertia forces increase with both blade length and rotation speed. In addition, [PERSON] and [PERSON] [5] studied parametric stiffness effects due to centrifugal and gravitational forces and found that the first eigenfrequency of a 100-m blade varied by up to 1.5% with the rotation angle. In all simulation studies, results varied strongly with parameters and respective models. [PERSON] et al. [8] summarised beam theories for rotating composite beams such as the Euler-Bernoulli beam theory, the classical beam theory and shear deformation theories with reference to influences such as material nonlinearities, shear stresses and centrifugal stiffening. Simulations form the basis for further development and testing but can never fully mimic blade properties and environmental conditions. Hence, vibration measurements on wind turbine blades in operation are needed to confirm simulations and investigate vibrations under real-world conditions. Even though few camera-based approaches were developed to monitor vibrations by means of semi-autonomous unmanned aerial vehicles (UAVs) [9] or multiple cameras in a test facility [10], mounting sensors directly on the blades is advantageous in sight-impairing conditions such as fog. In addition, it allows for continuous monitoring during the lifetime of the turbine. In a small-scale test, [PERSON] et al. [11] conducted a benchmark study and tested accelerometers, force sensors and strain gauges on a 1.75 m turbine blade in a climate chamber. They demonstrated the temperature variability of the frequency characteristic of the blade and proposed further investigation to consider those influences. Moreover, [PERSON] and [PERSON] [12] excited a 34 m blade on a test rig by means of an electromechanical actuator inside the blade and collected the vibrational response with an array of accelerometers. Their developed unsupervised learning method was able to detect a 20 cm damage on the trailing edge based on a damage indicator and a statistical model of the healthy blade. [PERSON] and [PERSON] [13] tested the same algorithm in a follow-up study in operation of a turbine and successfully identified damage. However, the study also demonstrates a major challenge of sensor mounting in real-world applications: Sensors were mounted on the outside of the turbine blade, which resulted in complicated wiring and potentially increased aerodynamic noise. In addition, [PERSON] and [PERSON] [14] applied active excitation to a 34 m blade on a test rig. Damage could successfully be detected; however, sensors had to be mounted in the vicinity of the damage location. In addition, [PERSON] et al. [15] studied environmental and operational effects with regard to damage detection on an operating Vestas V27 turbine.