## 1 Introduction
However, the CMS of WTs collects vibration signals regularly, so it is difficult to collect the run-to-failure data at a certain fixed speed. This time variation in speed will affect the degradation rate of the bearing and increase the amplitude fluctuation of working parameters in condition monitoring, which is a great challenge to the RUL prediction of practical engineering WT generator bearings [21]. Domain adaptive (DA) is an important branch of transfer learning. DA maps the features of different working conditions into a new feature space with the same distribution as much as possible, which is an effective solution for predicting bearing RUL under different working conditions [22, 23, 24]. [PERSON] et al. [25] used selective convolutional RNN to extract features from vibration signals and proposed a co-operative domain alignment method to learn domain-invariant features. [PERSON] et al. [26] proposed a deep feature disentanglement transfer learning network to reduce the distribution differences of features under different working conditions. [PERSON] et al. [27] used multi-kernel maximum mean discrepancy (MMD) to achieve DA under different working conditions. The methods mentioned above primarily involved transferring models from one working condition to another. However, the full life data of WT generator bearings is a combination of multiple working conditions. It is not realistic to establish a model for each working condition. More importantly, it is impossible to separate the run-to-failure data with equal time intervals and roughly the same working conditions for training the model. Therefore, designing an RUL prediction method for WT generator bearings under composite working conditions is of great significance for practical engineering applications. So, a local enhancement time convolutional model with multistage degenerate distribution matching based on domain adaptation (MDA-LETCN) for WT generator bearing RUL prediction is proposed in this paper. This method proposes local enhancement TCN to learn the temporal features of generator bearing data and uses the K-means for unsupervised and divides the bearing degradation state into three stages: healthy state, slight degradation, and severe degradation state. Secondly, a multistage degradation stage distribution matching (MDSDM) is set up, and MDSDM aligns the distribution of features at different degradation stages, promoting the model to learn the degradation laws at different stages under composite working conditions. Finally, part of the health data of the target bearing is used to reduce the degradation of individual differences in the generator bearings of different WTs by DA, and the model is transferred to adapt to the target bearing. Experimental verification was conducted using actual WT life cycle data from a wind farm in Shandong and compared with advanced methods. The results showed that the prediction accuracy of the proposed method is higher. The novelties of this work are as follows:  
1. RUL prediction methods for WT generator bearings under composite working conditions are proposed, which utilizes TCN with local enhanced residual module to extract temporal features and unsupervised K-means to partition the degradation status of generator bearings. 2. Aiming at the problem that the prediction accuracy of the full life data of WT generator bearings is affected by the cross-fusion working conditions, the MDSDM module is proposed, which can reduce the influence of composite working conditions on the model and help the model learn the degradation features independent of working conditions. 3. In view of the different degradation trends of WT generator bearings caused by working conditions, fault types, and other reasons, the DA module is added to the model to improve the prediction accuracy of the model in the target domain bearings. The rest of this paper is structured as follows. Section 2 introduces WT generator bearing data, data preprocessing, and the construction method of HI. Section 3 presents the details of the MDA-LETCN.