An adaptive method for predicting bearing remaining useful life across various degradation stages

Abstract Bearing degradation is a multi-stage, multi-trend and highly complex process, significant information discrepancies and extreme imbalances exist in degradation data across different stages. These complexities hinder the accuracy of predictive model in predicting the remaining useful life (RUL) throughout all stages of the bearing’s degradation. In this paper, a novel prediction model based on adaptive convolutional neural network (ACNN)-multiple kernel convolutional long short-term memory (MKConvLSTM) is proposed, which utilizes adaptive feature extraction and multi-scale dynamic selection to solve the problem of multi-stage, multi trend and highly complex information in bearing degradation. First, the ACNN is used to perform convolutional feature extraction and adaptive mapping on input samples, effectively distinguishing the degradation stages. Then, the MKConvLSTM generates features at different time scales and dynamically selects these features to capture temporal information during the degradation process, enriching the model’s capability to represent complex information and improving its predictive performance. To validate the effectiveness of the proposed model, experiments were conducted on the PHM2012 datasets and XJTU datasets. The MAE and RMSE of ACNN-MKConvLSTM reaches 0.078 and 0.099 on the first dataset, 0.086 and 0.107 on the second dataset, respectively. Approximately 20% improvement Compared to the baseline model, respectively. Experimental results demonstrate that the model exhibits high accuracy and robustness in bearing RUL prediction, effectively addressing the impact of feature variations across different degradation stages on prediction performance.