{"id":"W1968486765","doi":"10.1115/imece2004-59349","title":"Gearbox Fault Detection Using Empirical Mode Decomposition","year":2004,"lang":"en","type":"article","venue":"","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hilbert–Huang transform; Hilbert transform; Wavelet transform; SIGNAL (programming language); Fault (geology); Instantaneous phase; Envelope (radar); Computer science; Fault detection and isolation; Time–frequency analysis; Wavelet; Vibration; Signal processing; Pattern recognition (psychology); Mode (computer interface); Algorithm; Artificial intelligence; Speech recognition; Spectral density; Acoustics; Computer vision; White noise; Telecommunications","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":[0.0004348532,0.0002432187,0.0002870248,0.0007438769,0.00006515635,0.0002982921,0.000187231,0.000280978,0.0006768645],"category_scores_gemma":[0.002021238,0.0001212438,0.0001590089,0.0003158591,0.000130415,0.0005758557,0.0002234089,0.0002184773,0.0001368594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001255023,"about_ca_system_score_gemma":0.0001318408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005060416,"about_ca_topic_score_gemma":0.0004118986,"domain_scores_codex":[0.9997707,0.00006865512,0.00001419313,0.00003941306,0.00009118741,0.00001583892],"domain_scores_gemma":[0.9994468,0.0003118695,0.00006744952,0.00005295239,0.0001045405,0.00001638641],"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":[0.000445878,0.0001858157,0.0168083,0.0002069284,0.0001182416,0.0003169024,0.0001625402,0.1338649,0.1531536,0.003219136,0.0009989809,0.6905187],"study_design_scores_gemma":[0.00001611387,0.00008407573,0.009664921,0.00001109799,0.00001615182,0.0001681089,0.0000319051,0.9670675,0.02107527,0.001064184,0.0007840888,0.00001641059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3532417,0.0004179875,0.644417,0.00008736838,0.00002731939,0.00002634479,0.00008210631,0.000602045,0.001098191],"genre_scores_gemma":[0.8930222,0.0001750907,0.1061768,0.00001212742,0.00001246192,0.0000146186,0.0001011973,0.00001532696,0.0004701123],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007438769,"threshold_uncertainty_score":0.002299786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01837593251710458,"score_gpt":0.3889476228447492,"score_spread":0.3705716903276446,"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."}}