Speed-guided diffusion probabilistic model integrated with variational autoencoder for fault diagnosis under limited data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Deep learning-based fault diagnosis has shown great potential in intelligent condition monitoring. However, its performance heavily depends on large amounts of labeled data, which are often scarce in real-world industrial scenarios, especially under varying speed conditions. The data limitations hinder the generalization ability of fault diagnosis models. To address this issue, we propose a novel speed-guided denoising diffusion probabilistic model integrated with variational autoencoder (SDPM-VAE) for generating high quality data. The speed signal is incorporated as a conditional prior through a cross-attention mechanism to preserve speed-dependent characteristics in the generated data. Additionally, a hybrid VAE and DDPM framework is proposed to enhance data quality while reducing computational cost. The coarse reconstructions and latent representations derived from the VAE are integrated into the reverse diffusion process. Experimental results on two gearbox datasets demonstrate that the SDPM-VAE outperforms four state-of-the-art methods in both the quality of the generated data and fault diagnosis accuracy, thereby validating its effectiveness for data augmentation under limited data conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 it