Remaining Useful Life Prediction via Frequency Emphasizing Mix-Up and Masked Reconstruction
Bibliographic record
Abstract
The prediction of the Remaining Useful Lifetime (RUL) of machines and tools is crucial in the realm of modern manufacturing and in the framework of Industry 4.0. Recent deep learning techniques offer an opportunity for the utilization of data-driven methods in RUL prediction. However, the persistent data scarcity issue presents a thorny bottleneck in the machinery RUL prediction tasks due to the high cost of labeled training data collection. In this paper, we propose an effective learning framework for RUL prediction consisting of two novel methodological modules to improve data utilization efficiency. The first module is to leverage the intrinsicallydecomposed frequency-domain information to boost the effective feature extraction with a novel-designedFrequency Emphasizing Mix-up Module (FEMM). The second one involves incorporating semi-supervised learning to make use of unrestricted domain unlabeled data to overcome the constraints associated with data scarcity where we introducedMasked Autoencoder Reconstruction Auxiliary Learning (MARAL)to the model. In addition, to better obtain temporal information, an LSTM temporal projection layer is designed. The proposed method was evaluated through experiments conducted on the C-MAPSS datasets, and our results show that the proposed method outperforms existing other methods in terms of both accuracy and effectiveness in the RUL prediction. In addition, our experimental results demonstrate that the proposed method is able to leverage unrestricted domain datasets to significantly boost model performance under low-data scenarios.
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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 | 0.000 |
| 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".