Speed-guided diffusion probabilistic model integrated with variational autoencoder for fault diagnosis under limited data
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".