{"id":"W4416922211","doi":"10.1109/access.2025.3639515","title":"A Machine Learning Model of Electro-Hydrostatic Actuators With the Low-Data Limit and Its Application to Fault Detection","year":2025,"lang":"","type":"article","venue":"IEEE Access","topic":"Hydraulic and Pneumatic Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Actuator; Nonlinear system; Fault detection and isolation; Bayesian optimization; Bayesian probability; Limit (mathematics); Uncertainty quantification; Leakage (economics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006892078,0.0006760886,0.0007145291,0.0004860307,0.0003737058,0.0007510168,0.001073989,0.001043742,0.001276389],"category_scores_gemma":[0.002334927,0.0004013815,0.0005592949,0.0004600809,0.0007344374,0.001183805,0.0007147695,0.001416266,0.0003770023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005309206,"about_ca_system_score_gemma":0.0008100325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004926085,"about_ca_topic_score_gemma":0.003220071,"domain_scores_codex":[0.9996715,0.00006244351,0.00001966597,0.00009969217,0.0001182651,0.00002855436],"domain_scores_gemma":[0.9992487,0.0004107066,0.0001298442,0.00004980826,0.0001411928,0.00001980081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002375614,0.00001718031,0.0005392407,0.00004795312,0.00001399801,0.00006467325,0.00003550967,0.9776684,0.00209864,0.00712013,0.0003104298,0.01205997],"study_design_scores_gemma":[7.091809e-7,0.000004211027,0.00005928527,0.000001645196,9.116621e-7,0.00000764639,0.000001150503,0.9989161,0.0001416835,0.000733281,0.0001318072,0.000001582957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01162545,0.0002139325,0.9859417,0.0002260166,0.00002500181,0.00002423834,0.0001074337,0.0001930569,0.00164313],"genre_scores_gemma":[0.8433999,0.0007625628,0.1484192,0.0001745162,0.0000739344,0.0003339185,0.0004250458,0.00006483348,0.006346129],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004926085,"threshold_uncertainty_score":0.009794831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01815419043392995,"score_gpt":0.2667492838902515,"score_spread":0.2485950934563215,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}