## 1 Introduction
[11] used continuous wavelet transform to obtain the time-frequency images of bearing vibration signals and then input the images into CNN to achieve the bearing RUL prediction. [PERSON] et al. [12] performed a short-time Fourier transform on multiple time signals of bearings to obtain time-frequency images and then used time-frequency images to train CNN models; the correlation between neighboring signals was used to obtain good prediction results. [PERSON] et al. [13] proposed a double-CNN structure, the first CNN was used to determine the starting degradation time of the bearing, and then the second CNN was used for RUL prediction. Compared with existing methods, it has higher accuracy. [PERSON] et al. [14] proposed a CNN based on attention mechanism and Bi-LTSM. The original signals are directly input to CNN to extract time-frequency domain features, then the time-frequency features are input to Bi-LTSM with an attention mechanism for learning and bearing RUL prediction. Although the studies mentioned above achieved satisfactory results, in the traditional deep learning model for bearing RUL prediction, multiple signal characteristics are not considered, which leads to inadequate comprehensiveness of bearing degradation failure information. Therefore, the deep learning-based bearing RUL prediction model considering features of multiple domains has become a research direction. [PERSON] et al. [15] proposed a deep learning method for multi-bearing RUL collaborative prediction based on CNN and multiple signal characteristics. [PERSON] et al. [16] proposed a two-stage approach for bearing RUL prediction, which consists of a fast search and finds of density peaks clustering algorithm for health state recognition and a multi-dimensional recurrent neural network model based on LTSM and GRU. The input of the model contains features of the time domain, frequency domain and time-frequency domain, but the features are gathered into one-dimensional sequences. Processing automatical completely without any parameters presetting is one of the highlights of this work. [PERSON] et al. [17] proposed a prediction method based on a self-organizing map (SOM) and back propagation network, in which two time-domain features are considered as well as three frequency-domain features of different bearing defects. In their work, a new effective indicator was extracted by minimum quantization error (MQE) derived by SOM to train the back propagation model in order to learn more high dimensional features of bearing degradation. The time-frequency representation method, which can extract advanced features hidden in signals, has been widely used for nonstationary signals. However, the model using only the 2D time-frequency features, such as the proposed methods in the literature [11; 12], lack enough robustness [18]. [PERSON] et al. [19] proposed a novel temporal convolutional network with a residual self-attention mechanism. The Hilbert-Huang Transform was adopted to extract the three-dimensional marginal spectrum, which contains time-domain features and time-frequency domain features as input of their proposed CNN model, which combined the causal convolution and dilated convolution. In the literature [18], [PERSON] et al. proposed a 3D convolutional neural network, and the sub-sequences of time-frequency features were extracted by continuous wavelet transform for training the 3D CNN model to learn more effective hidden features. In this paper, a new P-ResNet framework-based intelligent RUL prediction method is proposed. Synchrosqueezed wavelet transforms (SWT) were adopted to obtain time-frequency images of raw collected data. The 1D time-domain characteristics and 2D time-frequency images were input into the P-ResNet model, which contains two parallel series of convolutional operations to learn the time-frequency features and time-domain features, respectively, in parallel. Adding residual layers to the deep network model to the 2D series of convolutional operation enhances the learning ability of 2D time-frequency domain features.