## 1 Introduction
### Challenges in Current Detection Methods  
A diverse range of methods and tools has been explored, encompassing sensor-based real-time monitoring technologies [7], advanced data analysis approaches [8], and visual inspection techniques [9]. Despite these advancements, there remain significant challenges and gaps:  
Sensor Deployment and Data Management: [PERSON] and [PERSON] [10] noted the challenges in installing sensors effectively for continuous monitoring. The difficulty lies not only in the installation but also in maintaining these sensors and managing the vast amounts of data they generate, especially in harsh and remote environments where wind turbines are typically located.  
Efficient Data Analysis: [PERSON] et al. [11] proposed a method using mechanical vibrations, reducing the number of samples needed for effective conclusions by 60%. However, the variability in turbine models and operational environments means that the analysis needs to be robust across different conditions, which remains a challenge. [PERSON] et al. [12] addressed the scarcity of samples for deep learning, proposing a semi-supervised fault diagnosis model that works efficiently with few samples, yet the model's accuracy under different conditions still requires further validation. In addition, [PERSON] et al. [13] proposed a graph attention autoencoder for blade icing supervision, which utilizes sensor data from supervisory control and data acquisition (SCADA) systems. This method shows promise but requires further testing under various operational conditions to fully validate its effectiveness.  
Visual Inspection Innovations: Visual inspection methods, including UAV-based techniques, have advanced significantly. [PERSON] et al. [14] and [PERSON] and [PERSON] [15] utilized UAVs and image analysis for fault detection, highlighting the potential of these methods. However, automating these inspections to reduce human error and ensuring consistent accuracy in various environmental conditions are ongoing issues.  
Integration of Acoustic and Vibration Signals: [PERSON] and [PERSON] [16], [PERSON] et al. [17], and [PERSON] et al. [18] proposed combining acoustic emission with vibration signals for detecting cracks and other faults. Although promising, these methods require more research to integrate effectively into comprehensive fault detection systems that can operate reliably in real-world scenarios.  
Machine Learning in Fault Detection: Machine learning has shown significant potential, particularly in processing large datasets from SCADA systems. [PERSON] et al. [19] used machine learning for real-time monitoring, and [PERSON] et al. [20] proposed an ARIMA model for predictive analysis. Additionally, [PERSON] et al. [21] integrated convolutional neural networks (CNNs) and frequency-time analysis to detect 62 blade faults with 100% accuracy for unbalanced and bearing faults. [PERSON] and [PERSON] [22] further emphasized the benefits of CNNs in wind turbine blade failure detection, showing that these networks reduce the need for human intervention and lower costs. However, these methods are often data-hungry and computationally intensive, which can be limiting factors in their widespread adoption.