170 / 2023-11-21 11:27:43
A novel Transformer model based on dynamic convolution and ProbSparse self-attention for RUL prediction of bearings
Remaining useful life prediction, Transformer, Dynamic convolution, ProbSparse self-attention mechanism, Rolling bearing
Final Paper
Yancheng Zhu / North China Electric Power University
Ling Xiang / North China Electric Power University
Hao Su / North China Electric Power University
Aijun Hu / North China Electric Power University
The health of rolling bearings is related to the normal operation of rotating machinery. Accurately predicting the remaining useful life (RUL) of bearings is the key to avoiding the failure of bearings and system. In this paper, a new dynamic convolution Transformer model with ProbSparse self-attention mechanisms is proposed to extract advanced degradation characteristics from complicated vibration signal for accurately RUL of bearing, which is called dynamic ProbSparse self-attention Transformer (DPT) model. First, the cumulative amplitudes of frequency domain are computed as the network inputs. Then, a dynamic convolutional layer is constructed in Transformer architecture with ProbSparse self-attention mechanism to enhance the long-distance feature capturing capability. Finally, the high-level representation is fed back to a linear regression network for estimating bearing’s RUL. The proposed DPT model is validated through using two datasets. Experimental results present that the proposed DPT network surpasses the other models in RUL predicting, which possesses higher precision and computational efficiency.

 
Important Date
  • Conference Date

    Nov 02

    2023

    to

    Nov 04

    2023

  • Dec 15 2023

    Draft paper submission deadline

  • Dec 20 2023

    Registration deadline

Sponsored By
IEEE Instrumentation and Measurement Society
Xidian University
Contact Information