Fault diagnosis in gearbox using adaptive wavelet filtering and shock response spectrum features extraction
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
A wavelet adaptive filtering technique is presented for enhanced fault identification in gearboxes. Based on Morlet wavelet analysis and conventional optimization methods, an adaptive filtering is performed for the background noise removal of vibration signals emanating from gearboxes. A fourth-order statistical moment, kurtosis, is used as an objective function to optimize. A filtered signal is obtained by choosing the suitable Morlet wavelet that maximizes the kurtosis. The optimization framework uses one-dimensional and multidimensional accelerated search techniques to speed up the convergence in solution search space. A novel, transient-based features extraction method based on the shock response spectrum is used to extract characteristic features representing the health state of the gearbox. The effectiveness and feasibility of the proposed method have been demonstrated on experimental gearbox data. The proposed technique enables a high signal-to-noise ratio for gearbox fault detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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