Robust Modeling of a Class of Vibration Signals Through Binary Neural Network-Based Symbolic Regression
Bibliographic record
Abstract
In this paper, we propose an automatic method to unveil the nonlinear multi-modulation model that describes vibration signal of certain complex systems. Firstly, we apply a harmonic estimation algorithm to the observed signal to identify all peaks in the spectral domain. Then, using the peaks' location information, a neural network-based symbolic regression is trained to determine a concise expression of the multi-modulation model underlying the considered signal. A regularized objective function is utilized to optimize the neural network weights and enforce their binarization (sparsity), providing a simple mathematical expression for the input signal model. The proposed algorithm improves the robustness of the determined model by eliminating any distortion in the input signal, such as transfer function and phase modulation distortion. This method could potentially be employed in the vibration analysis of rotating machines, such as planetary gearboxes, due to the structural spectral contents of the vibration signal, to reflect the interaction model between system elements.
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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".