.png){kind=icon}
.png){kind=icon}
Return
Uncertainty-guided alignment for unsupervised domain adaptation in regression
.png){kind=icon}
OA
AI
DOI：10.1016/j.ress.2025.112143.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In prognostics and health management systems, models must reliably predict asset health conditions across varying operating conditions, equipment manufacturers, or degradation patterns. However, obtaining labeled data for every new operational context is often impractical, particularly for run-to-failure trajectories. This work addresses this challenge through Unsupervised Domain Adaptation for Regression, which enables adaptation from a labeled source domain to an unlabeled target domain. Traditional feature alignment methods (such as adversarial or moment matching) underperform in PHM regression tasks due to the inherent correlation among learned features, leading to unreliable prognostic predictions when operating conditions change.
Keywords:
Unsupervised domain adaptation
Regression
Uncertainty estimation
Deep learning
Computer vision
Battery state of charge
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
R
IF:
11
Papers:
736
Citations:
0
Organization
No organization information available