{"id":"W2084206538","doi":"10.1155/2014/592080","title":"Monitoring Machines by Using a Hybrid Method Combining MED, EMD, and TKEO","year":2014,"lang":"en","type":"article","venue":"Advances in Acoustics and Vibration","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies; Natural Sciences and Engineering Research Council of Canada; Mitacs; National Natural Science Foundation of China; Pratt & Whitney","keywords":"Hilbert–Huang transform; Demodulation; Energy operator; Instantaneous phase; SIGNAL (programming language); Vibration; Energy (signal processing); Bearing (navigation); Entropy (arrow of time); Envelope (radar); Acoustics; Amplitude modulation; Computer science; Amplitude; Frequency modulation; Engineering; Electronic engineering; Artificial intelligence; Bandwidth (computing); Mathematics; Telecommunications; Physics; Statistics","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":[0.0006443415,0.0006939777,0.0006967636,0.001811569,0.0001986687,0.0005694963,0.0003745818,0.0006148126,0.0009553637],"category_scores_gemma":[0.0009318658,0.0002873328,0.0002957602,0.0009124607,0.0003747029,0.001277786,0.0006539063,0.0002878433,0.0004348757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001944728,"about_ca_system_score_gemma":0.0001457256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001238585,"about_ca_topic_score_gemma":0.000312863,"domain_scores_codex":[0.9995574,0.00006760324,0.00003399363,0.000105843,0.0002134287,0.00002169542],"domain_scores_gemma":[0.9995592,0.0001585184,0.00008237963,0.00005634366,0.0001223557,0.00002115958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004054969,0.000148696,0.005511618,0.000472577,0.00008115236,0.0001196195,0.0001219892,0.008625405,0.4024698,0.001431761,0.0005733357,0.5800385],"study_design_scores_gemma":[0.0001152378,0.001158246,0.02808473,0.0001001746,0.000212678,0.002623115,0.0002499973,0.5570003,0.3898514,0.003957517,0.01641299,0.0002337114],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06019279,0.0006251766,0.9365036,0.0001221584,0.00006678917,0.00008237734,0.0001253278,0.0008889145,0.001392918],"genre_scores_gemma":[0.4189584,0.0004295073,0.5783918,0.000110956,0.00007143846,0.0000770959,0.0001620505,0.00006278095,0.001736031],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001811569,"threshold_uncertainty_score":0.003407657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0067043665581276,"score_gpt":0.3067245934610958,"score_spread":0.3000202269029682,"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."}}