{"id":"W4401510550","doi":"10.3390/s24165186","title":"A Modified EMD Technique for Broken Rotor Bar Fault Detection in Induction Machines","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hilbert–Huang transform; Fault (geology); Rotor (electric); Fault detection and isolation; Signature (topology); Computer science; Noise (video); Induction motor; Data acquisition; Bar (unit); Engineering; Artificial intelligence; Control engineering; Pattern recognition (psychology); White noise; Voltage; Electrical engineering; Actuator","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002477539,0.0002019761,0.0001804224,0.0004209588,0.00003396269,0.00005497254,0.00009770199,0.0002119784,0.00001722994],"category_scores_gemma":[0.00008910729,0.0002022071,0.00008939031,0.0003221542,0.00001590548,0.0001450944,0.00001653977,0.0003118142,0.00001670565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001846538,"about_ca_system_score_gemma":0.000009056456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002384268,"about_ca_topic_score_gemma":0.0001982652,"domain_scores_codex":[0.9990922,0.00003420389,0.0002592687,0.0002644966,0.0001104763,0.0002393912],"domain_scores_gemma":[0.9996322,0.00008992949,0.00001616813,0.0001924016,0.0000262035,0.00004304637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005669805,0.00007331157,0.000314388,0.001287937,0.0000845863,0.00003481027,0.0007000738,0.06946688,0.767078,0.001032002,0.003797237,0.156074],"study_design_scores_gemma":[0.0001549539,0.00006585551,0.0005993086,0.0001468871,0.00001474487,0.00002404138,0.00001869927,0.5099325,0.4778256,0.001873863,0.009054354,0.0002891643],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9261816,0.0002487013,0.06536639,0.0001457714,0.000681483,0.002246254,0.00004532198,0.003404234,0.001680297],"genre_scores_gemma":[0.9933819,0.00006060956,0.004397314,0.00001500466,0.0002168356,0.001705781,0.00001530996,0.00008626133,0.0001209649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4404657,"threshold_uncertainty_score":0.8245766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01131162240514585,"score_gpt":0.2797999934197258,"score_spread":0.26848837101458,"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."}}