## 1 Introduction
### Related Work  
The fatigue testing process of wind turbine blades generates a large amount of data. These data contain rich patterns and characteristics, requiring effective analytical methods to unlock its potential value. Data-driven approaches can extract features from input data without relying on extensive prior knowledge, thus enabling predictions of Remaining Useful Life (RUL) [5]. Data-driven modeling methods can be categorized into statistical data-driven methods and machine learning methods [6]. Compared to statistical data-driven methods, machine learning methods can effectively extract deep features from monitoring data to achieve life prediction, avoiding the large errors caused by selecting inappropriate models. Furthermore, they do not significantly increase the training difficulty as the data dimension increases, making machine learning a widely explored and applied approach in the life prediction and health management fields in recent years. By training models with historical data, machine learning methods can be used for damage identification, reliability analysis, and prediction [7; 8]. Among these, neural networks (NNs) have been extensively explored in the prediction. They possess many appealing characteristics, including the ability to model nonlinear relationships, perform large-scale parallelism, and approximate functions universally. [PERSON] et al. [9] used an unsupervised learning network based on Neural Ordinary Differential Equations (Neural ODE) and Variational Autoencoders (VAE) to predict the fatigue residual stiffness of fiber-reinforced polymer composites, demonstrating that artificial neural networks outperform traditional mechanical and phenomenological models. [PERSON] [10] proposed a method for predicting residual stiffness on the basis of latent variable interpolation using latent Ordinary Differential Equation (ODE) neural networks and partial data retraining. [PERSON] proposed a BP neural network optimized by the Chimp Optimization Algorithm (ChoA), significantly improving the accuracy of wind turbine tower vibration prediction [11]. Due to these advantages, neural networks have been widely applied in industrial fields, particularly in predictive maintenance strategies for composite structures. In the wind energy sector, the accurate fatigue prediction of fiber-reinforced polymer composites plays a crucial role in optimizing wind turbine blade maintenance schedules and extending service life. Additionally, neural networks have been employed for the real-time structural health monitoring of composite components, enabling condition-based maintenance and reducing operational costs. The ability of neural networks to effectively learn underlying degradation patterns makes them a key tool for enhancing the structural reliability and efficiency of composite-based applications across various industrial sectors. Over the past few years, deep learning (DL) has proven effective in solving intricate prediction challenges. DL overcomes the inherent limitations of traditional neural networks and can better capture nonlinear features, enhancing RUL prediction accuracy. Modern DL models consist of various combinations of fundamental structures, including Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). Although RNN can capture dependencies in time series, they often face the problem of vanishing or exploding gradients while dealing with extended sequences, limiting their effectiveness in predicting distant states. To address this issue, the Long Short-Term Memory network (LSTM) was proposed [12]. LSTM has demonstrated superior performance compared to statistical and machine learning approaches, including AutoRegressive Integrated Moving Average (ARIMA), Support Vector Machine (SVM), and conventional neural networks [13]. CNN, which was originally developed for image processing, has recently been applied to time series analysis.