Service life prediction method for in-service bearings based on the improved CNN-LSTM model
HAN Yuntong
WANG Jingyue
HOU Xingda
DING Jianming
Abstract：[Objective]Aiming at the problems of complex parameter adjustment and limited prediction accuracy in traditional convolutional neural network-long short-term memory(CNN-LSTM)models,an improved remaining useful life prediction method was proposed to enhance the accuracy and stability of life prediction for in-service rolling bearings.[Methods]Firstly,the golden sine strategy was integrated into the golden sparrow search algorithm(GSSA)to improve its global and local search capabilities,enabling adaptive optimization of key parameters in the CNN-LSTM model.Secondly,a feature screening system based on correlation,monotonicity,and robustness was constructed to select highly sensitive degradation features.Finally,using the PHM2012 bearing dataset,a GSSA-CNN-LSTM prediction model was established,and its effectiveness was validated through comparisons with back propagation(BP)neural network and CNN-LSTM model.[Results]The results showed that the proposed GSSA-CNN-LSTM model reduced the root mean square error,mean absolute error,and mean square error by 67.61%,83.71%,80.89%and 61.18%,78.78%,51.02%,respectively,compared with the BP neural network and CNN-LSTM models,while the determination coefficient was closer to 1,demonstrating significant improvements in prediction accuracy and robustness.
Keywords：Rolling bearingGolden sine strategySparrow search algorithmRemaining life predictionOptimization
Publication Date：2026-02-15
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：7( 40-46 )