Multi-Source Domain Generalization for Machine Remaining Useful Life Prediction via Risk Minimization-Based Test-Time Adaptation
Bibliographic record
Abstract
Due to unnecessary access to the target data during training, domain generalization (DG) has received great attention in remaining useful life (RUL) prediction for rotating machines. However, existing methods often fail to estimate the ubiquitous adaptation gap, which intractably minimizes the generalization risk. In this study, a novel multi-source DG method is proposed for cross-domain RUL prediction, which considers adaptation gap and performs test-time adaptation to minimize the risk of generalization. Initially, multi-head domain-specific regressors are pretrained to learn the hypothesis from multi-source domains separately. Afterward, the test-time model selection and ensemble is utilized to collaboratively minimize adaptation gap, wherein two strategies of domain similarity and predictive indicator are presented to dynamically integrate the optimal regressor adapted to target domain. Meanwhile, the multioutputs integrated pseudo-labels are used to retrain and optimize the model. Experimental studies indicate that the proposed approach is promising with a maximum 13.05% improvement on prediction performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".