## 1 Introduction  
With the increasing global energy demand, wind power generation, a clean and renewable energy source, is becoming a key component in optimizing energy structures [1]. The spindle bearing of a wind turbine is a critical component. It operates in a complex environment for extended periods, making it prone to mechanical wear and environmental vibrations. Due to the difficulty in detecting faults promptly and the high maintenance costs, improving diagnostic efficiency is essential. The effectiveness of fault diagnosis methods directly impacts the operational stability and economic efficiency of wind turbines [2]. Therefore, it is vital to develop a feasible method for diagnosing spindle bearing faults, visualizing diagnostic results, and conducting condition monitoring to ensure the safe operation of wind turbines. While traditional fault diagnosis methods have yielded positive results in laboratory settings, they face challenges in real-world applications, including difficult data acquisition, high signal noise, weak fault characteristics, and heavy reliance on expert experience. The phase diagram analysis method forms distinct distribution areas where different faults correspond to specific regions in the phase plane trajectory. This approach has shown advantages in wind farm applications, including non-invasiveness and the ability to operate without prior system state information. However, it has limitations in handling complex fault modes and multivariate collaborative fault diagnosis [3]. Internet of Things (IoT) technology enables continuous equipment monitoring by tracking critical parameters, such as voltage and current, in real time. This provides strong support for fault location. However, IoT systems may encounter data processing challenges when handling large-scale data, including noise, storage issues, and transmission delays. Additionally, network transmission limitations of IoT devices may result in delays in real-time analysis and fault identification [4]. In recent years, with the advancement of artificial intelligence and big data technologies, deep learning methods have gradually been applied to fault diagnosis. By leveraging deep neural network models, these methods can automatically extract fault features from large datasets of vibration data, enabling accurate fault classification and improving diagnostic accuracy. Deep learning requires two conditions: first, the training dataset must have the same distribution and operating conditions as the test dataset, and second, a large amount of labeled data must be available. However, the operating environment of wind turbines is complex, and the distribution of real-world working conditions often differs significantly from laboratory data, limiting the diagnostic model's generalization ability. To address this issue, transfer learning has been introduced into the field of fault diagnosis. By transferring knowledge from the source domain to the target domain, transfer learning can alleviate the issue of insufficient data and enhance the model's generalization ability and diagnostic performance. [PERSON] et al. [5] constructed a ResNet-based migration network model with a shared domain adaptation regularization term using the maximum mean discrepancy (MMD) as the domain adaptation loss function. This approach reduced the distribution gap between the source and target domain equipment monitoring data. However, the method typically assumes that the data distribution difference between the source and target domains remains relatively stable, which may be more complex in practice, particularly when dealing with different devices or operating environments. [PERSON] et al. [6] employed the joint maximum mean difference (JMMD) to adapt specific features, facilitating the migration of the source domain training model to the target domain and resolving the issue of limited training samples in the target domain.