## 1 Introduction
[18] proposed an improved stacked denoising autoencoder method, which extracts features from noisy signals through the encoder and reconstructs the signal using the decoder to achieve effective signal denoising. At present, the above method has the advantages of automatic feature extraction and improved prediction accuracy in signal noise reduction processing, but there are also challenges, such as high algorithm complexity and great demand for parameter adjustment, and it is difficult to determine the method parameters adaptively, resulting in increased sensitivity of the model to noise, and the effect of the remaining useful life prediction model needs to be further improved. Feature extraction and health index construction are the key steps of remaining useful life prediction of rolling bearing. [PERSON] et al. [19] proposed a depth feature extraction method based on multi-dimensional self-attention temporal convolutional networks and designed a pattern-weighted feature fusion method to obtain degradation indicators. [PERSON] et al. [20] proposed a health index construction method based on the canonical resolution autoencoder model and predicted the remaining useful life of rolling bearings through the RUL prediction model based on a particle filter. [PERSON] et al. [21] proposed a data-driven feature extraction method, namely the fitting curve derivative method of maximum power spectrum density. This method extracts the performance degradation features of rolling bearings throughout the life cycle from historical data, thereby establishing a RUL prediction model. [PERSON] et al. [22] proposed a multi-sensor health indicator (HI) construction method based on reinforcement learning, which can realize automatic learning and find the best sensor combination rules, thereby improving the RUL prediction performance of the model. [PERSON] et al. [23] proposed a method for constructing composite health indicators by weighted fusion of multi-source sensors to characterize the evolution trend of equipment degradation and achieve remaining useful life prediction. [PERSON] et al. [24] used the self-organizing mapping method to extract features based on similar sample sets and proposed a health indicator construction method based on the minimum feature circle. The existing methods have achieved certain results in feature extraction and health indicator construction, with high efficiency and accuracy, but there are still some shortcomings, such as the single-domain feature is difficult to fully express the nonlinear degradation law of equipment in feature extraction, and the interaction between features is not fully considered in feature selection. Traditional feature dimensionality reduction methods based on linear models [25; 26] cannot adequately capture complex dynamic characteristics in nonlinear and non-stationary vibration signals, which may lead to information loss and other problems during feature dimensionality reduction. The construction of the remaining useful life prediction model has an important impact on the prediction effect. [PERSON] et al. [27] proposed a remaining useful life prediction method combining a self-attention mechanism with a long short-term memory neural network to solve the problem that the correlation between components was not fully considered in the RUL prediction of mechanical equipment. [PERSON] et al. [28] proposed a remaining useful life prediction model based on a transformer, which can simultaneously extract features of different sensors and time steps in parallel and finally verify the model performance using turbofan engine data sets. [PERSON] et al. [29] proposed an attention-based gated recurrent unit neural network model to effectively use feature information to predict the remaining useful life of equipment. [PERSON] et al.