# An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction
## 3 Methodology
### LSTM-AdaBoost Ensemble Learning and Prediction Model
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{13}{c}{**No feature Selection**} & \multicolumn{13}{c}{**PCA**} & \multicolumn{13}{c}{**mRMR**} & \multicolumn{1}{c}{**mRMR**} & \multicolumn{1}{c}{**FOSF \# Aware Struct**} & \multicolumn{1}{c}{**FOSF**} & \multicolumn{1}{c}{**mRMR**} & \multicolumn{1}{c}{**FOSF**} & \multicolumn{1}{c}{**mRMR**} & \multicolumn{1}{c}{**FOSF**} & \multicolumn{1}{c}{**mRMR**} & \multicolumn{1}{c}{**FOSF**} & \multicolumn{1}{c}{**mRMR**} \\ \hline \multicolumn{13}{c}{**Dataset**} & \multicolumn{13}{c}{**LSTM**} & **LSTM-AdaBoost** & **LSTM** & **LSTM** & **LSTM** & **LSTM** & **LSTM** & **-AdaBoost** & **LSTM** & **LSTM** & **LSTM** & **LSTM** & **LSTM** & **LSTM** & **-AdaBoost** \\ \multicolumn{13}{c}{**Predicted Absolute**} & **Predicted Absolute** & **Baseline** & **Predicted Absolute** & **Baseline** & **Predicted Absolute** & **Baseline** & **Predicted Absolute** & **Baseline** & **Predicted Absolute** & **Baseline** & **Predicted Absolute** & **Baseline** & **Predicted Absolute** & **Baseline** & **Predicted Absolute** \\ \multicolumn{13}{c}{**



## 5 Conclusions

The bearing RUL prediction accuracy largely depends on the performance of the degradation indicator set. This paper proposes an ensemble learning method to improve the prediction accuracy of bearing RUL. We mainly studied the feature extraction phase and prediction modelling phase in the process of bearing RUL prediction. For the feature extraction phase, a three-stage feature selection method was proposed to construct the bearing degradation indicator set; for the prediction modelling phase, AdaBoost algorithm was used to enhance the LSTM neural network. Finally, the features of the bearing degradation indicators set were input into the LSTM-AdaBoost prediction model for ensemble learning and robust prediction. Through experimental verification, the proposed method was applied to the XJTU-SY bearing datasets, and the method was compared with different feature selection methods and different prediction modelling methods. The results showed that the method can effectively predict bearing RUL. [PERSON] designed research and conceptualization; [PERSON] performed research and verified the proposed method; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript. This research was supported by National Natural Science Foundation of China under grant 71962004. The authors declare no conflict of interest. ## References

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