## 1 Introduction  
Due to the insufficient bending strength, tensile strength and other parameters of wind turbine blade material, as well as their overly large dynamic load, blade cracking is a common cause of failure, accounting for approximately 30% of all downtime accidents [1; 2; 3]. However, due to constantly changing speeds and transient impacts, modeling the failure mechanism of blade cracking proves very challenging. Furthermore, since blades account for roughly 15-20% of total equipment costs, blade cracking can lead to potentially enormous maintenance expenses and safety hazards. Given its importance and costs, timely and accurate approaches to detecting blade cracking are attracting widespread research attention [4; 5; 6]. Data-based detection approaches work independently of the dynamics and kinematics of physical systems, and are thus effective for blade cracking detection. However, data are generally imbalanced, with very few cracking samples in real scenarios, leading to detection approaches that support normal samples and rarely identify cracking samples in the early stage (theoretical analysis in Section 2.1). Aiming at such problems, current data-level solutions mainly include sampling and generative strategies. Sampling strategies, e.g., SMOTE (synthetic minority oversampling technique) and ADASYN (adaptive synthetic), utilize prior knowledge about the space distribution of collected cracking samples to synthesize new samples. [PERSON] [7] adopted 29 sensor features as original input and used SMOTE to synthesize virtual cracking samples via linear interpolation between two random real cracking samples. Meanwhile, the density and concentration area are introduced to increase the confidence of virtual cracking samples. [PERSON] [8] designed a synthetic and dependent wild-bootstrapped oversampling technique for wind turbine fault diagnosis which is a modification of SMOTE and mimics the temporal dependence of time-series data. In the data analysis part, two datasets collected from two wind farms of northeast and northwest China are used, with 70 attributes in total, including wind speed, x-axis nacelle vibration, yaw angle, etc. [PERSON] [9] dealt with imbalanced wind turbine data using the random oversampling technique, which directly removed some random normal samples to achieve amount balance. To verify the proposed method, 19 real variables covering 19 different attributes were used, including generated power, R-phase voltage, wind speed, etc. [PERSON] [10] designed a minority clustering SMOTE approach for wind turbine fault detection, which achieved different clusters of minority samples and solved the problem of the uneven distribution of fault samples. [PERSON] [11] adopted ADASYN to alleviate the critical imbalance and convolutional LSTM-GRU to recognize blade icing status. The dataset for verification was collected over 341.88 h with 26 sensor values, including wind speed, yaw position, vertical acceleration, etc. Generative strategies estimate the probability density of cracking samples and then sample new data points to supplement cracking samples. [PERSON] [12] designed a deep convolutional generative adversarial network (GAN) to produce a threshold for a condition monitoring scheme of wind turbines. The employed signal was the frequency spectrum transformed by fast Fourier transform. [PERSON] [13] used a generative adversarial network to transform normal data into rough fault data, and furthermore, a refiner developed using a GAN was adopted to make them much similar to real fault data. For verification, the input data contained 28 variables, such as pitch angle, hub angle and generator torque. [PERSON] [14] introduced sparse dictionary learning into an adversarial variational auto-encoder to generate virtual data and determine the posterior distribution from six sensor variables, including wind speed, active power, generator speed, three-phase current and voltage.