Research on gear fault diagnosis method based on SSA–VME–MOMEDA
Bibliographic record
Abstract
As a common mechanical part, gear is easy to be damaged because of its complex working environment, which can impact the running of the whole transmission device. Thus, it is very important to evaluate the health of gears in time. A gear fault diagnosis method based on multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and variational modal extraction (VME) is proposed to solve the problem that the periodic fault features of gears are difficult to be completely extracted from signals. Meanwhile, sparrow search algorithm (SSA) is introduced to optimize the initial parameters of VME and MOMEDA. First, SSA serves to hunt for the best α of VME, VME serves to obtain the signal near the gear fault frequency, and then SSA serves to hunt for the best L and T values of MOMEDA, and MOMEDA serves to strengthen the gear impact features. Finally, the gear impact features are extracted by envelope spectrum. Simulation and experiment show that this method can extract gear fault components from noise effectively with good results.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".