A dynamic spatial-temporal graph transformer with multi-frequency attention for remaining useful life prediction
Bibliographic record
Abstract
Abstract The remaining useful life (RUL) prediction is essential for cost-effective production and reliable predictive maintenance in intelligent manufacturing. Existing deep learning-based approaches often struggle to capture complex degradation patterns across temporal, spatial, and frequency domains. To address this limitation, a dynamic spatial-temporal graph transformer with multi-frequency attention (DSTGT-MFA) is proposed in this paper for RUL prediction. The proposed DSTGT-MFA model consists of three key components: a multi-scale gated convolutional neural network for extracting hierarchical local features, a graph convolution transformer for modeling long-term spatial-temporal dependencies with dynamic and static adjacency matrices, and a multi-frequency spatial-temporal attention mechanism to enhance temporal and spatial attention in the frequency domain. This integrated architecture enables the model to comprehensively capture degradation trends and fuse multi-domain features. Extensive experiments conducted on the commercial modular aero-propulsion system simulation (CMAPSS) and new CMAPSS datasets demonstrate that the DSTGT-MFA model achieves superior prediction accuracy compared to twelve baseline methods.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".