Vibration‐ and acoustic‐emissions based novelty detection of fretted bearings
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the use of a new feature reduction technique with novelty detection on vibration and acoustic‐emission sensors monitoring bearings mounted in the test benches of automotive manufacturers. Design/methodology/approach Signals from standard accelerometers and acoustic‐emission sensors were gathered from bearings operating under steady conditions on an accessory‐drive test bench. The bearings under test were subject to a variety of faults including fretting. These signals were processed and reduced to standard feature vectors, the dimensionality of which was reduced using a new principal‐component‐like technique optimized for novelty detection. The reduced data were analyzed with a novelty detection technique called the Support Vector Data Descriptor. Findings The classification results from these sensors, after being reduced with the proposed feature reduction technique, are substantially improved over those achievable with only standard novelty detection; nearly zero‐percent classification error was achieved. Research limitations/implications The feature reduction technique depends, in part, on the availability of the fault type in question – potentially violating the normal novelty detection assumption of limited abnormal data. This may require the manufacturer to gather real or simulated fault data prior to running tests. Practical implications Incipient faults may be detectable at a much earlier stage in a manufacturer's component failure analysis. Test engineers may use this technique to reliably automate the fault detection process and enable improved root‐cause analysis through the earlier identification of faults. Originality/value The application of the feature reduction technique will provide manufacturers and researchers with a new means of improving fault detection in machinery components.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".