Self-supervised learning for tool wear monitoring with a disentangled-variational-autoencoder
Bibliographic record
Abstract
The use of end-to-end deep learning in machinery health monitoring allows machine learning models to be created without the need for feature engineering. The research presented here expands on this use in the context of tool wear monitoring. A disentangled-variational-autoencoder, with a temporal convolutional neural network, is used to model and trend tool wear in a self-supervised manner, and anomaly detection is used to make predictions from both the input and latent spaces. The method achieves a precision-recall area-under-curve (PR-AUC) score of 0.45 across all cutting parameters on a milling dataset, and a top score of 0.80 for shallow depth cuts. The method achieves a top PR-AUC score of 0.41 on a real-world industrial CNC dataset, but the method does not generalise as well across the broad range of manufactured parts. The benefits of the approach, along with the drawbacks, are discussed in detail.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".