Parameterized Adaptive Matching of Generalized Demodulation Transform for Signals with CrossFrequency Trajectories
Bibliographic record
Abstract
Time-frequency analysis (TFA) is widely used in the mechanical equipment fault diagnosis, however, it is difficult to handle multi-component signals with current methods, especially those with cross-frequency trajectories. This paper develops an iterative method called parameterized adaptive matching of generalized demodulation transform (PAMGD). It works by a series of angle parameters to calculate the timefrequency representation (TFR) of the signal. Multi-indicator fusion is proposed to guide the selection of angles at each moment point. The angles matched to the signal are retained for generating the final TFR. The signal components with higher amplitude is iteratively estimated and the residual signal is updated by removing the detected mode. PAMGD clearly shows TFR with multiple components and cross-frequency trajectories. The effectiveness of PAMGD in processing multi-component signals is proved by simulations and experimental signals.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".