# An Attention-Based Method for Remaining Useful Life Prediction of Rotating Machinery
## 4 Conclusions  
In remaining useful life prediction of rotating machinery, prediction performance and reliability are often undermined by imbalanced data of machinery performance states and differences in probability distribution. To address these problems, an attention-based rotating machinery RUL prediction method is proposed herein.  
First, an unsupervised data clustering method for classification of rotating machinery performance state evolution stages is put forth. According to the rotating machinery performance states, the performance evolution is classified into three stages: a \"healthy stage\", \"degradation start stage\", and \"intensified degradation stage\". To solve the problem of fuzzy boundaries and overlaps of representation data regions, the distance between the data clusters and the \"healthy state\" cluster is calculated to establish a correspondence relation between the data cluster and the bearing performance state in order to classify similar samples in the bearing life dataset. Experiments show that each classified category of data accurately corresponds to the bearing performance state and can provide a basis for similarity weight calculation of the input data to the attention-GRU model.  
An expected attention calculation method for input data based on the similarity function is proposed, and an attention module for input data extraction is established, which is fused with a gated recurrent unit (GRU) to build an attention-GRU model for RUL prediction of rotating machinery. Comparisons with other baseline models show that the attention-GRU model achieves higher prediction accuracy and stability than other models. Our attention-GRU model strengthens attention to the local target features, solves the problem of imbalanced data that may negatively affect the prediction performance, and provides a solution to rotating machinery RUL prediction despite insufficient sample data.  
The RUL prediction models for rotating machinery discussed in this paper are mainly constructed based on large sets of sample data. However, in real-world scenarios, RUL prediction of equipment, especially under complex and unfavorable working conditions,  
Figure 11: Comparison of prediction results of Attention-GRU and LSTM.  
is principally a modeling problem, with few available samples. Therefore, future work will focus on the RUL prediction of rotating machinery with limited availability of sample data. Given the complexity of such problems, the attention-GRU RUL prediction model proposed in this paper will face challenges in terms of accuracy and efficiency. For this reason, models focusing on feature mining, such as a dual-channel attention mechanism, will be the research focus for feature extraction in RUL prediction when sample data are insufficient.  
Conceptualization, Y.D. and C.G.; methodology, C.G.; software, Z.Z.; validation, L.Z., Z.Z. and S.L.; formal analysis, C.G.; investigation, S.L.; resources, Y.D.; data curation, X.L.; writing--original draft preparation, C.G.; writing--review and editing, Y.D. and C.G.; visualization, L.Z.; supervision, Y.D. and X.L.; project administration, Y.D.; funding acquisition, Y.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 52175457), Guangdong Basic and Applied Basic Research Foundation (Grant No. 2022B1515120053) and the Key-Area Research and Development Program of Guangdong Province (Grant No. 2019B010154001).  
Not applicable.  
The data used to support the findings of this study are included within the article.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.