PROPOSING A NEW ACOUSTIC EMISSION PARAMETER FOR BEARING CONDITION MONITORING IN ROTATING MACHINES
Bibliographic record
Abstract
Bearings are important machine parts and their condition is often critical to success of an operation or process, hence there is a great need for periodic knowledge of their performance. According to reported research works in the past several years, it is believed that the extracted information from acoustic emission (AE) signals can be used for bearing condition monitoring. In this work, a novel parameter based on using the ratio of AE mean (μ) and AE standard deviation (σ), formulated as μ/σ is proposed to distinguish between lubricated and dry bearings. A heavy duty test rig was used in experimental work. Various levels of radial loads and rotational speed (ω) were applied to rotating shaft, which is connected to rolling element bearings. It was found that, except few cases, regardless of various levels of radial loads used, at higher levels of rotational speed, dry and lubricated bearings can be clearly distinguished when using proposed parameter.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".