Accurate predictions of the Remaining useful life(RUL)of mechanical equipment are vital for lowering maintenance costs and maintaining equipment reliability and safety.Datadriven RUL prediction methods have made signi...Accurate predictions of the Remaining useful life(RUL)of mechanical equipment are vital for lowering maintenance costs and maintaining equipment reliability and safety.Datadriven RUL prediction methods have made significant progress,but they often assume that the training and testing data have the same distribution,which is often not the case in practical engineering applications.To address this issue,this paper proposes a residual useful life prediction model that combines deep learning and transfer learning.In this model,called transfer convolutional attention mechanism for early-life stage time convolutional network(TCAM-EASTCN),an unsupervised domain adaptation strategy is introduced based on the characterization of subspace distances and orthogonal basis mismatch penalties in the convolutional attention mechanism for early-life stage time convolutional network(CAMEASTCN).This approach minimizes the distribution differences between different domains,enhancing the learning of cross-domain invariant features and effectively reducing the distribution gap between the source and target domains,thereby improving the accuracy of RUL prediction under varying conditions.Experimental results demonstrate that TCAMEASTCN outperforms other models in terms of RUL prediction accuracy and generalization.展开更多
基金supported in part by the Key Research and Development Program of Shaanxi Province under Grant 2020GY-104in part by the Key Laboratory of Highway Construction Machinery of Shaanxi Province,Key Laboratory of Road Construction Technology and Equipment(Chang'an University),MOE,under Grant 300102250503in part by the Fundamental Research Funds for the Central Universities under Grant CHD 300102250503.
文摘Accurate predictions of the Remaining useful life(RUL)of mechanical equipment are vital for lowering maintenance costs and maintaining equipment reliability and safety.Datadriven RUL prediction methods have made significant progress,but they often assume that the training and testing data have the same distribution,which is often not the case in practical engineering applications.To address this issue,this paper proposes a residual useful life prediction model that combines deep learning and transfer learning.In this model,called transfer convolutional attention mechanism for early-life stage time convolutional network(TCAM-EASTCN),an unsupervised domain adaptation strategy is introduced based on the characterization of subspace distances and orthogonal basis mismatch penalties in the convolutional attention mechanism for early-life stage time convolutional network(CAMEASTCN).This approach minimizes the distribution differences between different domains,enhancing the learning of cross-domain invariant features and effectively reducing the distribution gap between the source and target domains,thereby improving the accuracy of RUL prediction under varying conditions.Experimental results demonstrate that TCAMEASTCN outperforms other models in terms of RUL prediction accuracy and generalization.
