# Research on the Remaining Life Prediction Method of Rolling Bearings Based on Multi-Feature Fusion
## 5 Conclusions  
In order to improve the real-time monitoring capability in industrial processes and accurately and efficiently identify the rolling bearings' state of health during operation, a multi-feature fusion-based rolling bearing RUL prediction method is put forward. The following conclusions are drawn from the experimental findings based on the PHM 2012 bearing degradation dataset and the XJTU-SY bearing accelerated life test dataset:  
1. A method for multi-sensor data fusion has been developed to combine data from many sensors based on their channels. This approach not only provides an effective fusion of multi-sensor data but also compensates for the limitations of standard prediction methods that rely only on data from a single sensor.
2. To address the issue of incomplete extraction of global feature information throughout the feature extraction procedure, this paper uses TCN, LSTM, and Transformer to construct a parallel multi-branch feature learning network, designs a parallel multi-scale attention mechanism to capture both local and global dependencies, and realizes the adaptive weighted fusion of the output features from three types of feature extractors. The shallow features obtained by the parallel feature extractor are then residually connected with the deeper features through the attention mechanism to improve the utilization efficiency of the before and after feature information so as to learn more comprehensive feature information.
3. Validation of the validity and generalization ability of the proposed method using the PHM 2012 bearing degradation dataset and the XJTU-SY bearing accelerated life test dataset. The experimental findings demonstrate that the proposed method can precisely forecast the RUL of a variety of bearings. Comparative experiments demonstrate that the proposed method has a reduced prediction error.  
Conceptualization, D.J. and G.Z.; methodology, G.Z.; software, G.Z.; validation, G.Z.; formal analysis, G.Z.; investigation, G.Z.; resources, G.Z.; data curation, G.Z.; writing--original draft preparation, G.Z.; writing--review and editing, G.Z.; visualization, G.Z.; supervision, D.J.; project administration, D.J.; funding acquisition, D.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Foundation of China (62263020), the Key R&D Program of Gansu Province (23 YFGA0061), the Lanzhou Science and Technology Plan Project (2022-2-69), and the Excellent Youth Foundation of Gansu Scientific Committee (20 JR10 RA202).  
The data used for training and test set PHM 2012 are available at: [[https://github.com/Lucky-Loek/ieee-phm-2012-data-challenge-dataset](https://github.com/Lucky-Loek/ieee-phm-2012-data-challenge-dataset)]([https://github.com/Lucky-Loek/ieee-phm-2012-data-challenge-dataset](https://github.com/Lucky-Loek/ieee-phm-2012-data-challenge-dataset)) (accessed on 20 January 2024). The data used for training and test set SJTU-SY are available at: [[https://github.com/WangBiaoXJTU/xjtu-sy-bearing-datasets](https://github.com/WangBiaoXJTU/xjtu-sy-bearing-datasets)]([https://github.com/[PERSON]/xjtu-sy-bearing-datasets](https://github.com/[PERSON]/xjtu-sy-bearing-datasets)) (accessed on 20 January 2024).  
The authors declare no conflicts of interest.