RUL Prediction With Cross-Domain Adaptation Based on Reproducing Kernel Hilbert Space

Data-driven methods for predicting remaining useful life (RUL) have received considerable attention in the field of degradation data analysis. The transfer learning (TL) method offers new possibilities for RUL tasks in various operational settings. However, in many engineering applications, challenges in TL arise mainly from the scarcity or high cost of labeled data in the target domain, coupled with incomplete degradation of RUL samples within the target domain. This article proposes an innovative model named deep cross-domain transfer learning for interpretable prediction The model effectively harnesses the advantages of domain adaptation (DA) techniques in mitigating domain distribution disparities and also uses the exceptional visualization capabilities inherent in the variational autoencoder (VAE) model. This method integrates the VAE framework with regression networks and utilizes DA techniques to align feature spaces, achieving cross-domain RUL prediction with unlabeled target domain data and cross-domain visualization of the entire degradation process. The reproducing kernel Hilbert space is considered in domain adaption to control the complexity of hypothesis space. The effectiveness of the proposed method is demonstrated by analyzing the real C-MAPSS dataset.