{"id":"W7116731162","doi":"10.1016/j.epsr.2025.112630","title":"Wind turbine fault diagnosis with fractional differencing-based feature enhancement and ensemble learning","year":2025,"lang":"en","type":"article","venue":"Electric Power Systems Research","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"State Key Laboratory of Synthetical Automation for Process Industries; Department of Education of Liaoning Province; Natural Science Foundation of Liaoning Province","keywords":"SCADA; Ensemble learning; Boosting (machine learning); Turbine; Wind power; Extreme learning machine; Feature selection; Classifier (UML); Feature extraction","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006653087,0.0005104102,0.00077663,0.0007966912,0.0003642777,0.0005183726,0.0006092878,0.0005454581,0.0008138023],"category_scores_gemma":[0.001770407,0.0002047877,0.0006939195,0.0007727705,0.0002006932,0.001024147,0.0005405915,0.000743631,0.0002302805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003000171,"about_ca_system_score_gemma":0.0004039615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002443138,"about_ca_topic_score_gemma":0.00283846,"domain_scores_codex":[0.9997441,0.00003564911,0.00002214327,0.00007266484,0.00008880635,0.00003663409],"domain_scores_gemma":[0.999406,0.0002334548,0.00004882886,0.000106229,0.0001875305,0.00001795442],"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":[0.0003766875,0.0001924499,0.00308953,0.0000715294,0.0001285846,0.0001101632,0.00007528644,0.1923762,0.03118161,0.002492967,0.001404953,0.7685001],"study_design_scores_gemma":[0.00000453271,0.00003813655,0.0008887688,0.000002750982,0.0000198531,0.0000319426,0.00000502943,0.9933892,0.004717329,0.0006177653,0.0002781948,0.000006442771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06040163,0.0003150392,0.9373685,0.00008598247,0.00008001458,0.00002826999,0.00007075141,0.00069751,0.0009523181],"genre_scores_gemma":[0.7880382,0.0001672638,0.209819,0.00005396663,0.00004543319,0.00004061164,0.0002306619,0.00004501261,0.001559794],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002443138,"threshold_uncertainty_score":0.004857838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01233733846784113,"score_gpt":0.3083646937574159,"score_spread":0.2960273552895747,"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."}}