## 1 Introduction  
Wind energy, as a renewable and clean energy, has developed rapidly worldwide. Wind turbines (WT) are an important equipment for converting wind energy into electricity. With the gradual increase in operating time, transmission chain failures gradually appear [1]. As one of the important parts of the transmission chain, the rolling bearing is affected by the wind speed and operates under the condition of variable speed for a long time, which is prone to failure, affecting the stability of the WT operation and even leading to casualties [2; 3; 4]. In addition, the replacement of rolling bearings has a long cycle and high cost. Consequently, monitoring the operational status of WT generator rolling bearings and predicting their remaining useful life (RUL) hold significant importance in preventing escalating maintenance costs and averting catastrophic accidents caused by bearing failures. WTs are usually equipped with condition monitoring systems (CMS) to monitor the operating status of key components of the transmission chain for a long time and periodically collect vibration signals [5; 6]. Because of the long collection time, the full life information can be collected, which provides the possibility for RUL prediction of rolling bearings of WTs. RUL prediction methods can be broadly categorized into two groups: physical model-based methods [7; 8] and data-driven methods [9; 10]. Physical model-based requires the development of precise physical models to describe the degradation process of WTs. However, the transmission chain structure of WTs is very complex and the working conditions are changeable, so it is difficult to establish an accurate physical model. Data-driven RUL prediction models have received increasing attention with the rapid development of artificial intelligence [11, 12, 13]. [PERSON] et al. [14] proposed a novel multihierarchy network based on multiordered neurons and realized RUL prediction for gearbox and bearings. [PERSON] et al. [15] developed a new health indicator (HI) for RUL prediction of bearings using a Bayesian optimization gated recurrent unit network (GRU). [PERSON] et al. [16] added two attention gates to GRU to construct a bidirectional structural prediction bearing RUL. However, all the methods used in the above research can be classified as the improvement of recurrent neural network (RNN), whose training mode is based on recurrent recursive structure and can only process one sample at a time, which leads to the shortcoming of long-term dependence and affects RUL prediction effect. Temporal convolutional neural network (TCN) represents an enhancement of convolutional neural network (CNN). By extending causal convolution, temporal features of historical data can be extracted, while also possessing the advantages of CNN's expandable acceptance domain and parallel computing. Compared with RNN structures such as GRU, TCN not only avoids information leakage from the future to the present but also improves prediction efficiency and has excellent prediction ability [17, 18]. [PERSON] et al. [19] proposed a competitive TCN that enhances feature extraction capabilities and improves the accuracy of the model's RUL prediction for rolling bearings. [PERSON] et al. [20] proposed a novel multiscale temporal convolutional transformer that takes into account both long-term and short-term potential degradation features of bearings, effectively capturing long-term dependency coupling while improving local feature learning ability. The superiority of this proposed method was empirically validated using experimental datasets. In the above studies, the effectiveness was verified using the laboratory accelerated bearing life dataset. However, the laboratory accelerated life experiments were all conducted under a single working condition, and the data structure was good. During the actual operation of WTs, the speed of the generator bearings varies with the wind speed.