[9] proposed a multi-objective optimization-based ensemble deep learning method for rotor-bearing diagnosis, integrating convolutional residual networks, deep belief networks, and deep autoencoders. [PERSON] et al. [10] introduced a physics-based deep learning approach that first assesses bearing health levels using a threshold model, followed by a CNN that automatically extracts high-level features from the inputs for bearing fault detection. However, in reality, obtaining a large amount of bearing fault data is often challenging. Additionally, the operating conditions of motors are complex, and vibration signals cannot consistently maintain linearity. These factors contribute to the poor performance of deep learning models in addressing these challenges. Transfer learning is an effective method that leverages existing knowledge to solve problems in different but related domains. It relies two fundamental assumptions of traditional deep learning: the requirement that the training and testing samples must be independently and identically distributed, and the necessity for a large number of samples to develop an accurate classification model. Consequently, transfer learning is used to tackle the issue of low fault diagnosis accuracy under varying operating conditions for bearings. In transfer learning, domain adaptation algorithms are commonly used to align the source and target domains. [PERSON] et al. (2018) proposed a cross-domain fault diagnosis framework based on transferable features and manifold embedding discriminant distribution adaptation. This method designs a transferability evaluation method using an adjusted Rand index and maximum mean discrepancy (MMD) to quantify the fault discriminability and domain invariance of features. Additionally, a new manifold-embedding discriminant joint distribution adaptation method is proposed to address the class imbalance problem between the target and source domains. [PERSON] et al. (2018) proposed a model based on the sliced Wasserstein distance for bearing fault diagnosis under different loads and speeds. This model achieves comprehensive domain adaptation by using adversarial training to learn a domain-invariant space. [PERSON] et al. (2018) introduced a multi-scale extension method based on residual neural networks, combined with the multiple kernel maximum mean discrepancy (MK-MMD), to address issues affecting rotating components such as bearings and gears in noisy and complex environments. [PERSON] et al. (2018) introduced a method based on a convolutional kernel dropping mechanism, skip connections, and joint maximum mean discrepancy (JMMD). This approach aims to improve diagnostic accuracy in unsupervised domain discrepancy scenarios by enhancing feature transfer and domain alignment. [PERSON] et al. (2018) proposed a model that integrates domain-adversarial neural networks with an attention mechanism. This model incorporates an attention mechanism into the feature extractor to retain fault-related features. Additionally, it replaces the fully connected (FC) layers in the classifier and discriminator with global average pooling layers, thus reducing the number of parameters and enhancing efficiency. [PERSON] et al. (2018) introduced the gradient conditional domain adversarial network method, which uses conditional domain adversarial networks (CDAN) as the main component and integrates data filtering and intermediate domain selection. The above article focuses on the application of domain-adaptive transfer learning in bearing fault diagnosis and proposes various domain-adaptive methods to address the differences in data distribution under different working conditions and complex environments.