Remaining Life Prediction of Shielding Sleeves Based on Data Augmentation and Hybrid Models

Remaining useful life (RUL) prediction is a critical procedure to avoid catastrophic failure of shielding sleeves and prevent nuclear safety risks. Affected by the structural characteristics and service conditions of shielding sleeves, it is difficult to obtain sufficient full-life-cycle actual degradation data, which greatly restricts the training and application of data-driven prediction models. This paper proposes a remaining useful life prediction method for shielding sleeves based on data augmentation and a hybrid model. Firstly, starting from the physical failure mechanisms of two typical failure modes of shielding sleeves, namely bulging and wear. Secondly, based on the analytical models of typical failures of shielding sleeves, a degradation data augmentation method using Monte Carlo simulation is proposed to address the problem of missing full-life-cycle degradation data. Finally, a hybrid RUL prediction model for shielding sleeves based on Stacking ensemble learning is presented, which integrates the advantages of physical degradation models and deep learning methods. Experimental verification is carried out through multiple sets of degradation datasets with different failure modes. The root-mean-square error (RMSE) of the proposed prediction method can reach a minimum of 0.0058, and the mean absolute error (MAE) can reach a minimum of 0.0044. The prediction accuracy is superior to that of single models, which verifies the prediction performance and engineering applicability of the proposed method.