A Machine Learning Model of Electro-Hydrostatic Actuators With the Low-Data Limit and Its Application to Fault Detection
Bibliographic record
Abstract
This paper presents a novel approach for modeling the dynamics of electro-hydrostatic actuators (EHAs) using the sparse identification of nonlinear dynamics (SINDy) algorithm. SINDy is a machine learning algorithm that identifies governing equations from input-output data. It is a suitable algorithm, especially when there is access to only a limited number of datasets. Due to the nonlinearity in dynamics, the SINDy algorithm can be a useful tool to provide a framework to accurately capture the complex dynamics of EHAs, even with limited data. One of the challenges when using the SINDy algorithm is tuning its hyperparameters. We employ Bayesian optimization to find the optimal parameters in the SINDy algorithm. To show the performance of the method, comprehensive simulations and experimental validations have been conducted. The proposed method demonstrates superior performance in modeling the behavior of EHAs under various inputs by only considering the input voltage, position, and velocity data. Deriving the dynamic equations for the EHA opens doors for a variety of other engineering applications, such as condition monitoring. The proposed SINDy modeling approach is further used for actuator leakage fault detection, which achieves a classification accuracy of 95.56% in distinguishing between healthy and faulty datasets under diverse conditions, including varying load and leakage levels.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".