# Research on the Remaining Life Prediction Method of Rolling Bearings Based on Multi-Feature Fusion
## 1 Introduction  
With the continuous development of the industrial level, the metallurgical industry plays an important role in the process of achieving comprehensive industrialization in China, which is of great significance to the industrial and economic development of the country [1]. In the metallurgical beneficiation process, the grinding link in mechanical equipment is crucial [2]. However, as metallurgical equipment works in harsh environments all year round, the longer the equipment is used, the more difficult it is to avoid wear, aging, and failure of rolling bearings [3, 4, 5]. Such wear and failure will not only affect economic production efficiency but may also cause accidents in serious cases, resulting in casualties and property losses. Therefore, the prediction of the remaining useful life (RUL) of rolling bearings is a meaningful and extremely important work [6]. In the past few years, many scholars have devoted themselves to the research of bearing RUL prediction methods. These methods can be broadly categorized into two primary groups: model-driven and data-driven approaches. Model-driven-based methods achieve bearing RUL prediction by constructing a physical or mathematical model that can accurately describe the bearing degradation process, which mainly include Kalman filtering [7], particle filtering [8], Wiener's process [9], Gamma process [10], and Weibull distribution [11] methods. The construction process requires not only the parameters of theactual engineering system obtained after a series of measurements but also a great deal of a priori knowledge. While model-based approaches are useful in predicting the general trend of mechanical degradation, it is difficult to accurately simulate the degradation trend with simple physical or mathematical models in practical industrial applications, especially for complex mechanical equipment. Due to the swift advancement of intelligent sensing and machine learning technologies, a substantial quantity of condition monitoring data is gathered in industrial production, leading to the quick growth and increased viability of the data-driven approach. Currently, machine learning and deep learning are the main research directions for data-driven methods [12]. In traditional machine learning approaches to unstructured data, such as text, images, or audio, a process of feature engineering is often required to transform the raw data into features with interpretable and representational capabilities. This process requires domain expertise and experience to select and design appropriate feature extraction methods. Deep learning has the ability to learn features with representational power directly from raw data through the learning capabilities of multi-layer neural networks. Compared to traditional methods, deep learning models possess the ability to autonomously extract and acquire abstract features of a higher level from unstructured data, eliminating the need for manual feature creation and selection. Therefore, with sufficient data, deep learning prediction methods are more effective than traditional machine learning and are now widely used in the domain of RUL forecasting [13]. Since [PERSON] et al. [14] proposed the deep learning theory, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and their derivative networks have gained wide application in the field of lifetime prediction. For example, [PERSON] et al. [15] introduced a health indicator (RNN-HI) based on RNN to forecast the RUL of bearings. [PERSON] et al. [16] combined an RNN-based estimation method with a filter-based state-space estimation technique to enhance the accuracy and precision of the RUL prediction for lithium-ion batteries. However, RNNs may suffer from gradient explosion during model training, so researchers have made many improvements to the RNN structure to solve the problem and have obtained many improved models. Among them, the Long Short-Term Memory (LSTM) model, introduced by [PERSON] and [PERSON] [17], is widely recognized as the most prominent in its field.