# Research on the Remaining Life Prediction Method of Rolling Bearings Based on Multi-Feature Fusion
## 1 Introduction
[PERSON] et al. [18] employed LSTM to construct a dual-task deep LSTM model for simultaneously learning the assessment of aero-engine degradation and predicting its RUL. Although LSTMs are capable of capturing temporal dependencies in time-series data both before and after a given point, their intricate chain structure results in longer training periods and less sensitivity to extended time series. To enhance the effectiveness of prediction models, certain researchers have endeavored to implement RUL prediction with CNN. [PERSON] et al. [19] developed a model based on deep CNN and LSTM for mining deeper information from limited data to accurately predict the remaining lifetime of lithium-ion batteries. Although CNNs provide a remarkable capability to extract features that are locally relevant, they lack sensitivity to temporal information and are prone to ignoring the back-and-forth correlation of temporal information in the data. In order to tackle this issue, [PERSON] et al. [20] proposed a solution to address this problem by introducing a Temporal Convolutional Network (TCN) that integrates causal convolution with null convolution. This network exhibits similar temporal feature extraction capabilities as RNN and has shown promising results in time series modeling applications. [PERSON] et al. [21] developed a TCN network that incorporates a soft threshold and attention mechanism to effectively capture important features and accurately forecast the RUL of mechanical devices. However, the convolution operation used in TCN, i.e., the extraction of local information, exhibits a deficiency in considering worldwide data and does not adequately account for relationships that span over a significant period of time in the sequence. In order to consider global correlations, some researchers have used Transformer to predict bearing RUL. [PERSON] et al. [22] used the Transformer encoder as the backbone of the model to capture short-term and long-term dependencies and global feature representations in the time series, which in turn predicts their remaining lifespan. In addition, in recent years, as smart manufacturing systems have improved, modern industries have increasingly implemented a significant number of signal sensors [23]. According to [PERSON] et al. [24], the use of many sensors in the industrial production process can result in the collection of a significant volume of data. This, in turn, can enhance the dependability of the health monitoring system for industrial equipment, so the information derived from multiple sensors is more valuable to study than the single sensor data. However, how to effectively utilize multi-sensor data and achieve fusion of feature information is still an open question. To summarize, while deep learning techniques have shown promising outcomes in predicting the RUL of bearings, there are still unresolved concerns that require attention:  
1. Most of the current research focuses on the utilization of single-sensor data, while insufficient attention is paid to the efficient integration and utilization of data from several sensors. Meanwhile, when using parallel networks for feature extraction, the same network structure is often adopted without giving full play to the advantages of multiple networks. 2. In most of the parallel attention mechanism structure research, each branch of the network utilizes the multi-head self-attention mechanism to adjust the internal connections inside the data. However, using the same feature extraction method to fuse the outputs of each branch network may result in important features being masked while redundant features are retained, ultimately affecting the overall performance of the network. In order to tackle the aforementioned issues, this research presents a method for predicting the RUL of rolling bearings by utilizing a fusion of many features.