On the use of time synchronous averaging, independent component analysis and support vector machines for bearing fault diagnosis
Why this work is in the frame
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Bibliographic record
Abstract
Condition monitoring of rolling elements bearings is investigated in this paper. Recently [11], we have shown that Time Synchronous Averaging combined with Support Vector Machines can lead to efficient bearing fault diagnosis. But the generalization performance of the SVMboundaries was strongly affected by the transmission path of the signals. This paper is then concerned with the integration of Independent Component Analysis (ICA) in this diagnosis procedure to improve its efficiency in such cases. First, we validate the use of TSA as a signal processing tool that will automatically highlight bearing defect frequencies if they are present in the envelope spectrum. Next, twenty classical features (rms, peak, crest factor…) are extracted from the envelope of the TSA-signal. To study the influence of Independent Component Analysis on the generalization performance of SVMboundaries, the twenty dimensional feature vectors are projected in their independent components space. The generalization performance of SVM-boundaries and the influence of signal transmission path as well as the faulty bearing location are then analyzed using these independent components.
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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 | 0.001 | 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 it