Lightweight Compressed Temporal and Compressed Spatial Attention with Augmentation Fusion in Remaining Useful Life Prediction
Bibliographic record
Abstract
Data-driven models for predicting the Remaining Useful Lifetime (RUL) have gained popularity due to their efficiency to enhance industrial security and reduce economic losses. Recently, there has been a notable rise in the research of transformer-based models for RUL prediction. While transformer-based models have shown significant improvements over previous LSTM-based and CNN-based models, we have raised concerns regarding high computational complexity, in-effective training with low data, no sensitivity to the order of the time series, and permutation invariant on its application to RUL prediction. The persistent issue of data scarcity and the importance of capturing the temporal relations in RUL prediction further question the suitability of transformer-based models. Considering these, We propose a simple non-transformer model, Compressed Temporal and Compressed Spatial (CTCS) Attention, which is efficient and lightweight, to capture both temporal and spatial information with the incorporation of pre- and post-positional encodings. Additionally, we introduce an Augmentation Fusion Module (AFM) to enhance the comprehension ability of the invariant characteristics of the data. The proposed methodology is evaluated on the NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and comprehensive experiments show that our proposed method not only surpasses other methods but outperforms the transformer-based model while requiring significantly fewer Floating-point operations (FLOPs), up to 32 times less.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".