Velocity Synchrosqueezing Windowed Fourier Transform for Fault Diagnosis of Fixed-Shaft Gearbox Under Nonstationary Conditions
Bibliographic record
Abstract
The Velocity synchrosqueezing transform (VST) is a good method to analyze the vibration signal for condition monitoring of planetary gearbox under non-stationary conditions. The VST is realized by jointly applying domain mapping, synchrosqueezing transform (SST) and time-frequency representation (TFR) restoration. It features a smear-free result for time-frequency analysis. However the VST has lower frequency resolution in the higher frequency region, this drawback limits its effectiveness in analyzing the vibration signal of fixed-shaft gearbox. To resolve this problem, this paper proposes the velocity synchrosqueezing windowed Fourier transform (VSWFT) method. Compared with the VST, this method employs the synchrosqueezing windowed Fourier transform (SWFT), instead of the SST, to process the angle-domain signal. As the SWFT uses window with fixed window length, it has fixed time-frequency resolution, which is more suitable for analyzing vibration signal of fixed-shaft gearbox. Finally the TFR is restored from the SWFT. The fault, if any, can be diagnosed by identifying the revealed sidebands of meshing frequency in the TFR. The effectiveness of the proposed method is validated using experimental vibration signal collected from a faulty gearbox under a non-stationary condition.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".