ArticleScientific reports2026
Remaining useful life prediction across different operational conditions based on domain adaptation feature transfer.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To address the challenge of data distribution discrepancy in cross-condition remaining useful life (RUL) prediction, this paper proposes a domain adaptation feature transfer framework. The framework integrates the ensemble empirical mode decomposition (EEMD) with deep denoising autoencoders (DAE) to extract degradation-sensitive features. Subsequently, transfer component analysis (TCA) is adopted to align the feature distributions between the source and target domains. Experimental results on the IEEE PHM 2012 bearing dataset demonstrate that the proposed method outperforms traditional statistical features and existing domain adaptation approaches. Specifically, the root mean square error (RMSE) and mean absolute percentage error (MAPE) are reduced by 21.3% and 39.1%, respectively. Further analysis verifies the robustness of the proposed method under noisy and small-sample scenarios, which highlights its potential application prospects in industrial fields such as wind turbine gearbox monitoring and aircraft engine maintenance.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.