Remaining useful life prediction using attention-based hidden physics-informed neural network and dual-path mixer model
Bibliographic record
Abstract
Remaining Useful Life (RUL) prediction is crucial for ensuring production continuity, optimizing maintenance strategies, and reducing operating costs. In recent years, data-driven approaches have become the dominant method for RUL prediction. However, these methods often suffer from low interpretability and limited accuracy. This paper proposes a hybrid framework, the Dual-path Mixer Model-attention-based Hidden Physics-Informed Neural Network (DPMM-AHPINN). The framework integrates the feature extraction capability of the DPMM with the AHPINN. This model leverages DPMM for spatio-temporal feature learning while using AHPINN to enforce physical consistency. Experimental findings on the C-MAPSS dataset indicate that DPMM-AHPINN attains substantial performance enhancements, especially in intricate operational conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".