SGAIN fusion TCN for predicting residual life of rolling bearings with missing data
As the net-work transmission faults and sensor miss-reading will cause the problem of data missing,to predict the rolling bearing service life more accurately under the condition of data missing,the paper gives a prediction method of residual life(RUL)that integrates the slim generative adversarial interpolation net-work(SGAIN)with the temporal convolutional network(TCN).Firstly,SGAIN is used to learn the distribution pattern of the missing dataset,to grasp the association between the existing data and the missing data so as to interpolate the missing data.Secondly,a rolling bearing residual life prediction model is established using TCN network.The interpolated completed dataset was used to achieve the residual life prediction of rolling bearings with missing data.Finally,the SGAIN interpolation method was compared with other interpolation methods using publicly available datasets,which the superiority of the SGAIN interpolation method was revealed.At the same time,the missing bearing data at 20%missing rate were selected to make the prediction of residual life.The results show that the interpolated data life prediction score reaches 0.722 2,which is 0.179 7 higher than the score of 0.542 5 for the missing uninterpolated data,and is closer to the original data life prediction score of 0.755 2.This shows that the rolling bearing residual life prediction method based on SGAIN integrated with TCN is effective.
rolling bearingsmissing dataSGAINTCNresidual life prediction
NETL
NSTL
万方数据
