Physics-guided ODE neural network for high-fidelity gearbox dynamics modeling based on vibration measurements
Bibliographic record
Abstract
High-fidelity dynamics modeling of gearboxes is the prerequisite for developing digital twins capable of elucidating failure behaviors under varying speed conditions. However, traditional approaches, such as finite element and lumped parameter models, often exhibit discrepancies from real-world measurements. This issue is particularly pronounced in complex systems, which limits their practical applicability. To overcome this limitation, we propose a novel physics-guided ordinary differential equation (ODE) neural network. This method integrates a neural network into the gearbox dynamics model to address model incompleteness, specifically the discrepancies between theoretical predictions and actual system behavior. Real acceleration measurements are utilized to calibrate both the neural network and the overall dynamics model, enabling the inference of unknown dynamic parameters without the need for prior determination. By aligning simulated responses with experimental data, the model captures system dynamics with high accuracy. The proposed physics-guided ODE neural network is fully differentiable with respect to both model incompleteness and undetermined dynamic parameters. The effectiveness of this high-fidelity modeling approach is demonstrated using an experimental two-stage gearbox system. Validation against experimental test rig data under varying rotational speeds and faulty conditions confirms the model capability to replicate real-world dynamic responses.
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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 | 0.001 | 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 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".