## 2 Methods
### Experimental Setup  
#### 2.3.1 Data Preprocessing  
The full-scale fatigue test of blades is essentially an accelerated fatigue test based on the principle of damage equivalence. The goal is to replicate the fatigue damage equivalently and complete the fatigue performance assessment within a testing period that is acceptable in engineering practice [3; 24]. During the full-scale fatigue test, if the stiffness variation in the blade is less than 10% and no significant damage, cracks, or local instability are observed, the blade is considered to meet design requirements. For simplicity, the blade is treated as a cantilever beam with isotropic material properties under constant load, and its stiffness can be expressed as:  
\[E=\frac{F}{L} \tag{5}\]  
In this formula, \(E\) represents the static stiffness of the blade, \(F\) is the concentrated calibration load applied to the blade, and \(L\) denotes the measured displacement at the critical section. During testing, the ambient temperature and humidity can influence the variation in blade stiffness. If temperature and humidity are taken into account, it is necessary to train stiffness along with these factors simultaneously. According to [PERSON]'s study [25], the changes in processed stiffness can reflect the impacts of temperature and humidity. Thus, stiffness alone is adequate for assessing the fatigue condition of blades. This method simplifies network training and facilitates increasing its depth to enhance performance [14]. In previous work by [PERSON] [3], experimental data at the critical point (32 m) of a 52.5 m glass fiber-reinforced plastic (GFRP) wind turbine blade were obtained. Table 1 lists the specific parameters of the blade. In this paper, this critical point is chosen as an example for stiffness prediction, predicting blade stiffness under different fatigue life ratios. Table 2 presents the measured normalized stiffness under different calibration loads and fatigue life cycle ratios. \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Value** \\ \hline Power & 2.5 MW \\ Blade length & 52.5 m \\ The length of the blade root & 2.4 m \\ Blade mass & 10,968 kg \\ Maximum chord length & 3 m \\ Airfoil & DU/NACA \\ Rated speed & 23 rpm \\ Main material & class fiber-reinforced epoxy resin \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Basic parameters of the blade. To better train the model for residual stiffness degradation prediction, this study adopts [PERSON]'s stiffness degradation model [26] to expand the dataset. According to [PERSON]'s model, the blade damage can be described as  
\[D(n)=\frac{E_{0}-E_{n}}{E_{0}-E_{f}}=1-\sin\left[1-\left(\frac{n}{N}\right)^{b} \right]^{a}\frac{\pi}{2} \tag{6}\]  
In this formula, \(E_{0}\) is the initial [PERSON]'s modulus, \(E_{f}\) is the failure [PERSON]'s modulus, \(E_{(n)}\) represents the [PERSON]'s modulus after the nth cycle, \(n\) is the corresponding number of cycles, and \(N\) is the fatigue life of the blade. Parameters \(a\) and \(b\) are material constants fitted from experimental data. Through Equation (6), the stiffness data in Table 2 can be converted into damage data while simultaneously fitting the parameters \(a\) and \(b\). Table 3 lists the fitted parameters \(a\) and \(b\), as well as \(E_{f}\) under five loading levels. Based on [PERSON]'s model, the model for stiffness at various load levels is formulated as follows:  
\[E(n)=E_{f}\bigg{\{}1-\sin\left[1-\left(\frac{n}{N}\right)^{b}\right]^{a}\frac{ \pi}{2}\bigg{\}}+\sin\left[1-\left(\frac{n}{N}\right)^{b}\right]^{a}\frac{\pi} {2} \tag{7}\]  
Figures 3 and 4 illustrate the blade damage curves and stiffness degradation curves, respectively, under different load conditions. Owing to the limited stiffness data obtained from experiments, DL methods typically need a substantial amount of samples to ensure the generalizability of the model. Therefore, stiffness degradation curve data serve as testing datasets to validate the effectiveness of this prediction approach.