A joint time-invariant wavelet transform and kurtosis approach to the improvement of in-line oil debris sensor capability
Bibliographic record
Abstract
In-line oil debris sensors are important devices for the detection of machinery failures. However, two key issues remain to be addressed to more effectively make use of the existing oil debris sensors: the responsiveness to early machine failures and false alarms. The responsiveness level depends on the size of the debris that can be detected by an oil debris sensor. The detectable particle size in turn is mainly limited by the background noise. The false alarms are often caused by spurious impulses such as vibration-like signals. The challenge of improving the responsiveness and reducing false alarms lies in the very weak particle signals and their similarity to spurious signals. In this paper, a joint time-invariant wavelet transform and kurtosis analysis method is proposed to address the two issues simultaneously. The proposed method has been tested by extracting signatures of ultra-small metal particles from background noise and a wide range of simulated vibration-like and real vibration signals. Our test results have demonstrated that the proposed method can effectively detect very weak particle signals buried in strong background noise and eliminate vibration-like spurious signals. The implementation of the proposed method will substantially enhance many existing oil debris sensors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".