{"id":"W4395674749","doi":"10.3390/math12091307","title":"Monitoring the Wear Trends in Wind Turbines by Tracking Fourier Vibration Spectra and Density Based Support Vector Machines","year":2024,"lang":"en","type":"article","venue":"Mathematics","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Turbine; Wind power; Downtime; Condition monitoring; Vibration; Reliability (semiconductor); Computer science; Drivetrain; Support vector machine; Spectral density; Control theory (sociology); Automotive engineering; Marine engineering; Power (physics); Environmental science; Engineering; Reliability engineering; Aerospace engineering; Acoustics; Torque; Physics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002633975,0.0001752209,0.000171497,0.0001438463,0.00004218749,0.0001762735,0.0001023247,0.00006958336,0.00006289558],"category_scores_gemma":[0.00004171603,0.0001319598,0.00004267219,0.0002456561,0.00002204326,0.0001583484,0.00002037734,0.0002190385,0.000005352878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004867784,"about_ca_system_score_gemma":0.00000616511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001626871,"about_ca_topic_score_gemma":0.00002445028,"domain_scores_codex":[0.9992686,0.00001798194,0.0002361366,0.0001450898,0.0001618545,0.0001703273],"domain_scores_gemma":[0.9995562,0.0001870723,0.00002064324,0.0001928611,0.00001003745,0.00003322195],"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":[0.00003675849,0.001009812,0.1348567,0.01042334,0.0005832601,0.0005132846,0.04609626,0.00758521,0.3395775,0.006943719,0.08755295,0.3648212],"study_design_scores_gemma":[0.0001833775,0.00004179424,0.01661188,0.0004409939,0.00005746509,0.00002576032,0.0000887522,0.8447506,0.1341137,0.002028111,0.001287116,0.000370493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.986627,0.0006307687,0.01000074,0.0006540535,0.0002636171,0.0001998382,0.00001722302,0.0007972268,0.0008095335],"genre_scores_gemma":[0.9889316,0.00004488178,0.01072068,0.00001601554,0.0001371727,0.0000210438,0.000009000091,0.00005070291,0.00006892237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8371654,"threshold_uncertainty_score":0.5381163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01166037534765581,"score_gpt":0.2716869101931569,"score_spread":0.2600265348455011,"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."}}