SAFO: Serial Amplitude Modulation–Frequency Modulation Operator for Multichannel Multicomponent Signal Decomposition and Its Application to Rolling Bearing Fault Diagnosis
Bibliographic record
Abstract
The amplitude modulation-frequency modulation operator (AFO) is an extension of the operator-based signal decomposition method, which uses the amplitude modulation-frequency modulation (AM-FM) signals as the basis functions. In this article, the single-channel AFO is extended to the multichannel case with the serial AFO (SAFO) proposed for multichannel signal decomposition. The proposed SAFO approach uses a concatenation technique to concatenate the multichannel signals into a single-channel signal and then the 1-D signal decomposition method AFO is used to deal with the obtained signal for obtaining several components. Ultimately, the final components are obtained by de-concatenating the 1-D components into multichannel status. SAFO preserves the original essence of AFO and its advantages. Then, a new fault diagnosis method for the rolling bearing is established based on the proposed SAFO for multichannel signal decomposition, the effective weighted sparse kurtosis (EWSK) indicator for the selection of modes containing rich feature information and the square envelope spectrum (SES) for fault information demodulation. Subsequently, the comparison of simulations and measured data is made and the analysis results demonstrate that the SAFO method is superior to the compared methods in the extraction of prominent and feature-rich fault features and fault diagnosis.
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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 | 0.001 |
| 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".