## 1 Introduction  
Wind turbine blades generate electrical energy by using the lift and drag of passing wind to rotate a structure connected to a generator shaft. The blade is composed of upper and lower skins that receive the wind energy, and a spar cap-shear web that increase the load resistance of, and prevent damage to, the skin [1, 2]. To address the resulting increase in the weight of the blade's structure, wind turbine components are generally manufactured using glass- and/or carbon-fiber-reinforced composites with high specific strengths and stiffnesses [3, 4]. When these composite structures are connected, mechanical bonding methods such as riveting or bolting may not only increase weight but also the stress concentration and resulting premature failure. Therefore, an adhesive-based bonding method is used to make the connections between these composite structures [5]. Unfortunately, adhesive-based joints have a high likelihood of developing debonding damage that separates the joints between members due to defects or unconsoldered factors in the design and manufacturing process, potentially causing the catastrophic failure of the wind turbine [6, 7]. In particular, the joint between the spar cap-shear web, which supports the entire load applied to the blade, is considered to be an early and dominant failure site that can lead to significant damage [7, 8, 9]. It is therefore important to have a methodology to identify possible debonding damage in advance of premature failure at the joints between the blade spar cap-shear web. There are many techniques to detect the damage in structures, including visual inspection, vibration analysis, ultrasound detection, thermal imaging, machine vision, and finite element analysis [10; 11; 12; 13; 14; 15]. Among them, the detection of damage by measuring the vibration frequency of a structure is based on the theory that the natural frequency of a structure might change with the change in its stiffness owing to internal damage. For example, [PERSON] et al. [12] proposed a damage detection technique using the natural frequency change of small wind turbine blades equipped with acceleration sensors. Furthermore, [PERSON] et al. [13] predicted the damage of a blade using the vibration responses acquired from modal analysis of data obtained from a test of small wind turbine blades. As the vibration analysis technique can detect both external and internal damage, it is considered particularly appropriate for detecting damage to composite blades joined using adhesive bonding. Although it is reasonable that the natural frequency changes due to the damage of the blade, there are some issues in detecting damage using natural frequency; the first is that the debonding damage has various configurations (location, length, and single or multiple damages) and the second is that the sensitivity of natural frequency of damage is very low. It should be noted that the natural frequency of a wind turbine blade changes in very complex ways depending on the various configurations of debonding damage. Thus, a more efficient analysis method is required to extract damage features from the change in natural frequency. One potential solution is an application of machine learning, which enhances the information-processing capacity of computer programs by training them with data and processing experiences [16; 17]. For example, [PERSON] et al. [18] suggested a machine learning-based damage-type classification technique for small wind turbine blades using their natural frequencies, and [PERSON] et al. [19] and [PERSON] et al. [20] introduced a machine learning technique for identifying damage in wind turbines. However, these studies were limited to the external damage of small blades and did not consider the structural characteristics of large blades, such as their extreme length or use of a spar capsular web, or the various factors affecting debonding damage.