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Research on remaining useful life prediction of rolling bearings based on adaptive variational mode decomposition and dual-branch temporal neural network 认领 引用
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作者 Wuchu Tang Wenxin Dong +1 位作者 Jiawei Yang Guofu Wu 《Advances in Engineering Innovation》 2026年第7期12-30,共19页
To address the nonlinear and non-stationary characteristics of vibration signals during rolling bearing operation and the issue of insufficient degradation information representation,this paper designs a prediction fr... To address the nonlinear and non-stationary characteristics of vibration signals during rolling bearing operation and the issue of insufficient degradation information representation,this paper designs a prediction framework that combines Adaptive Variational Mode Decomposition(AVMD)with a SETCNBiGRU multi-head temporal attention mechanism for Remaining Useful Life(RUL)prediction.First,AVMD is used to decompose the bearing horizontal vibration signal into five Intrinsic Mode Functions(IMFs).Timedomain and frequency-domain statistics are extracted from each IMF and concatenated into a 115-dimensional degradation feature sequence.Subsequently,the model processes in parallel:a TCN-SENet branch extracts local temporal features and adaptively adjusts channel weights,while a BiGRU with multi-head temporal attention sub-network captures global bidirectional dependencies and critical degradation periods within the degradation sequence.Finally,the two types of features are fused,and the RUL prediction result is output.Experimental results demonstrate that the proposed model achieves an RMSE,MAE,and R2of 0.0582,0.0477,and 0.9483 respectively on the IEEE PHM 2012 dataset,and average values of 0.0780,0.0559,and 0.9133 on a self-built laboratory bearing dataset,indicating good prediction accuracy,robustness,and generalization ability. 展开更多
关键词 remaining useful life prediction adaptive variational mode decomposition temporal convolutional network bidirectional gated recurrent unit multi-head temporal attention mechanism
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