{"id":"W7108069179","doi":"10.1139/tcsme-2025-0060","title":"Cross-condition fault diagnosis method for hydraulic systems based on domain adaptation and ensemble learning","year":2025,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Hebei Province","keywords":"Fault (geology); Hydraulic machinery; Testbed; Domain (mathematical analysis); Artificial neural network; Convolutional neural network; Kernel (algebra); Domain adaptation; Ensemble learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009235769,0.00072357,0.0007934176,0.0009956928,0.0003391352,0.0004520361,0.0007340179,0.0007294272,0.0009710621],"category_scores_gemma":[0.001808631,0.0002444677,0.0006903816,0.0004915483,0.0003402008,0.0008348572,0.0008424143,0.001050196,0.0003142836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004489306,"about_ca_system_score_gemma":0.0006082295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002931733,"about_ca_topic_score_gemma":0.002820441,"domain_scores_codex":[0.9996276,0.000057837,0.00002543966,0.0001251556,0.0001176875,0.00004622889],"domain_scores_gemma":[0.9991585,0.0002823344,0.0001014324,0.0001232071,0.0002943784,0.00004002367],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001942019,0.0001275017,0.005406042,0.00008008463,0.000135478,0.0002003731,0.0001750137,0.3730118,0.02705361,0.00288789,0.002226802,0.5885012],"study_design_scores_gemma":[0.000002750106,0.00002520924,0.0007419093,0.000002773267,0.000008326727,0.00005353706,0.000009626167,0.9942356,0.003896257,0.0007119181,0.0003055835,0.000006647604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02203341,0.0001638242,0.9762068,0.00005588748,0.00002776885,0.00002391499,0.00002515536,0.0009213418,0.0005418042],"genre_scores_gemma":[0.7544919,0.0001285825,0.2432044,0.0001018517,0.00003911871,0.00006907158,0.0002103787,0.00007854097,0.001676118],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002931733,"threshold_uncertainty_score":0.005829334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01025644365382936,"score_gpt":0.2714769397113775,"score_spread":0.2612204960575481,"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."}}