Experimental Study of Low-Speed Gearbox Faults Using Vibration and Acoustic Emission Signals
Bibliographic record
Abstract
With the goal of finding an effective condition monitoring tool for low-speed gearbox applications, vibration-based tests and Acoustic Emission (AE) tests have been performed on several gearbox sets in normal and faulty conditions. Vibration based tests were conducted both on-site and off-line. Vibration signals from the gearboxes were mostly corrupted by background noise, as was demonstrated by the close proximity of dominant frequencies in the frequency spectrum. Among those, 121.5 Hz was observed as the most significant dominant frequency for both on-site and off-line test regardless of speed changes. This frequency response was speculated to be a property of electrical noise. The AE tests were conducted on faulty and normal gearboxes at different shaft speeds from 3 to 35 rpm. Hit-based, time-driven and frequency domain AE parameters were used to compare their effectiveness. The count rate, absolute energy and signal strength were found to be good hit-based parameters in this application. The count rate was the best hit-based parameter with the largest parameter difference value at a shaft speed of 25 rpm. The absolute energy was observed to be the best time-driven parameter. Both frequency and time-frequency analysis results indicated that the faulty gearbox has higher spectral peaks in the lower frequency range, which was confirmed numerically by the frequency centroid difference. AE was shown to be superior to vibration signal analysis in condition monitoring of low-speed gearbox faults with numerous AE parameters identified as showing significant differences between normal and faulty gearbox conditions.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".