Hydro-generators fault diagnosis with short-time-wavelet-entropy and variational auto-encoder
Bibliographic record
Abstract
Abstract The prognosis and health management (PHM) of hydroelectric plants are full of difficulties caused by the complexity of the hydro-generators where each machine is different and almost unique. At industrial level, several tools are used to monitor the generator condition. Among these tools, the measurement of magnetic stray flux is one which is gaining interest. This measurement is generally based on an inductive sensor and mainly mounted near the stator. The main advantages of the magnetic stray flux are the non-invasive nature and the simplicity of its implementation. In this work, the discrete wavelet transform (DWT) is used to decompose the stray flux signal. Short-Time-Wavelet-Entropy (STWE) is then applied to extract the features from the sub-bands. Finally, a variational auto-encoder (VAE) is used in an unsupervised learning process to structure the STWE signatures of more than 400 stray flux measurement collected on real hydroelectric plants. The obtained results show that the VAE has well captured the features from the wavelet entropy (WE) signatures. An analysis of the resulting latent space shows a strong correlation between a given trajectory in the reduced space and an increase of the WE.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".