Normalization of gearbox vibration signal for tracking tooth crack severity progression under time-varying operating conditions
Bibliographic record
Abstract
It is a challenge to track gear tooth crack severity progression under time-varying operating conditions since time-varying operating conditions induce amplitude modulation (AM) and frequency modulation (FM) effects into gearbox vibration signals. The AM and FM effects induced by time-varying operating conditions can mask tooth crack signature, which results in difficulty in distinguishing between changes of tooth crack severity and changes in gearbox working conditions. The reason for this difficulty is that due to the AM and FM effects, changes in condition indicators (CIs) may indicate changes of tooth crack severity, changes in gearbox operating conditions, or both. To overcome this challenge, AM and FM effects induced by time-varying operating conditions need to be removed. For FM effect, it can be removed using order analysis. In this study, a novel normalization method is proposed to conduct removal of the AM effect. The effectiveness of the proposed normalization method for removing AM effect is verified using simulated gearbox vibration signals, which benefits tracking tooth crack severity progression under time-varying operating conditions.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".