Effects of Parameter Selection on Oscillatory Behavior-Based Signal Decomposition for Bearing Fault Signature Extraction
Bibliographic record
Abstract
Oscillatory Behavior-based Signal Decomposition (OBSD) is a new technique which employs Morphological Component Analysis (MCA) and the Tunable Q-factor Wavelet Transform (TQWT) to decompose a signal into components consisting of different oscillatory behaviors rather than different frequency bands or scales. Due to the low oscillatory transients of bearing fault-induced signals, this method shows promise for application to effectively extract bearing fault signatures from raw signals contaminated by interferences and noise. In this paper, the application of OBSD to bearing fault signature extraction is investigated. It is shown that the quality of the results obtained via the OBSD is highly dependent on the selection of method-related parameters. The effects of each parameter on the performance of the OBSD for bearing fault signature extraction are investigated. The analysis is also validated by implementing the OBSD on experimental data collected from a test rig with a defective bearing.
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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 | 0.002 | 0.021 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".