Transferable cross-domain adaptive learning for aircraft engine remaining useful life prediction
In the task of predicting the remaining useful life(RUL)of aviation engines,the scarcity of labeled data and the variability of operating conditions result in significant distribution differences among sensor time series,hampering the generaliza-tion ability of RUL prediction methods.Cross-domain learning offers a feasible solution to this challenge.Traditional cross-domain learning minimizes the distribution discrepancy between the source and target domains to obtain aligned features,facilitating cross-domain knowledge transfer.However,as aviation engines degrade,the semantic information between consecutive time steps changes,causing local semantic mismatches in the previously aligned features,which adversely affects model performance.To ad-dress this issue,this paper explores a transfer adversarial approach for cross-domain RUL prediction,optimizing the probability en-tropy of local domain discriminator outputs to make aligned features indistinguishable at the local level.The method utilize the tar-get mutual information during RUL prediction to impose semantic constraints,resulting in domain-invariant features with both local transferability and target semantic importance,thus enhancing the model's generalization ability.Experimental results on the CMAPSS aviation engine dataset demonstrate the effectiveness of this approach,outperforming existing cross-domain adaptation methods in terms of RMSE and SCORE metrics.
remaining useful life predictioncross-domain learningdomain adaptationtransferable adversarial
NETL
NSTL
万方数据
维普
