Prediction method for bearing remaining life based on GMTCN model

Abstract

Abstract:

Aiming at the problem that the existing prediction methods for bearing remaining life are difficult to effectively

extract degradation features when dealing with multi-sensor data, a prediction method for bearing remaining life

based on global attention and multi-scale time convolutional network (GMTCN) was proposed. Firstly, the GMTCN

model was used to process the multi-sensor signals of the bearing, and the degradation features of the bearing at

different scales were extracted with the help of two different strategies of temporal convolutional networks.

Secondly, the global attention mechanism was used to balance the contribution of data from different sensors and

time steps in the bearing remaining life prediction, and the extracted multi-scale features were fused. Finally, the

remaining life of the bearing is predicted. To assess the performance of this method, remaining life prediction were

conducted using the PHM2012 bearing dataset and a bearing dataset obtained from degradation data collected on

an accelerated fatigue testing platform. The root mean square error (RMSE) and mean absolute error (MAE) values

obtained were lower than other methods, while the average value of SCORE was increased to a certain extent,

proving the effectiveness of the method.

Key words:

bearings, remaining useful life, temporal convolutional network, feature extraction, attention mechanism

CLC Number:

TH133.3

GUO Runxiaa , JIANG Yuyanga , HUANG Chaob.

Prediction method for bearing remaining life based on GMTCN model

[J]. Journal of Civil Aviation University of China, 2025, 43(3): 15-23.

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