A novel method based on wavelet transform and prototypical network for gearbox detection in few-shot learning
Bibliographic record
Abstract
Fault diagnosis is crucial for industrial systems, with traditional methods such as CNN heavily reliant on large training datasets to achieve high accuracy. However, such datasets are often-times inaccessible in the real world. Even in few-shot learning models, such as Model-Agnostic Meta-Learning (MAML), the quantity of training data significantly impacts the stability and accuracy of the models, posing challenges for reliable fault diagnosis under limited data conditions. To address these issues, the Wavelet Transform Prototypical Network (WTPN) is proposed, which integrates discrete wavelet transform with prototypical networks for limited training dataset. There are two main structures in WTPN. Firstly, this method transforms one-dimensional vibration signals into two-dimensional distance matrices, enhancing feature extraction and classification accuracy. Secondly, a confidence weighting mechanism assigns weights to decomposed signals based on their classification reliability, thereby improving consistency and reducing performance variability. Then, results from both experimental and publicly available datasets validate that WTPN consistently outperforms existing few-shot learning models in terms of accuracy and stability. Furthermore, the contributions include enhanced feature extraction through DWT, improved stability via confidence weighting, and robust performance in scenarios with limited training data. In conclusion, WTPN represents a significant advancement in fault diagnosis, offering reliable outcomes with minimal training data, making it particularly suitable for applications where data availability is constrained.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".