Rolling Bearing Life Prediction Combining TA-TCN and Transfer Learning
Aiming at the problem that the accuracy of remaining life prediction of rolling bearings is not high due to the small amount of data in actual industrial production,a time convolutional neural network(TCN)optimized by temporal attention(TA)and transfer learning is proposed.Firstly,the original eigen-vector is decomposed into a set of subsequence components by empirical mode decomposition of comple-mentary sets(CEEMD)to highlight the eigensignal and reduce noise interference.Then,the subsequence components are input into the built TCN model and TA is added for optimization,and the relationship be-tween the depth features and the degradation curve is deeply explored.Finally,transfer learning is intro-duced to obtain the target network model by using the source domain data for training and a small amount of target domain data for parameter fine-tuning.After example verification,the stability and prediction accu-racy of the proposed model are improved compared with other comparison models,and it still has good pre-diction ability under different working conditions.
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