{"id":"W3194661270","doi":"10.1109/tim.2021.3107009","title":"A Smart Sensor-Based cEMD Technique for Rotor Bar Fault Detection in Induction Motors","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Data acquisition; Fault detection and isolation; Fault (geology); Rotor (electric); Engineering; Hilbert–Huang transform; Induction motor; Sideband; Condition monitoring; Electronic engineering; Computer science; Control engineering; Electrical engineering; Actuator; Voltage; Radio frequency","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":[0.0002851025,0.0001829245,0.0001530679,0.0002896708,0.0001061117,0.00003965634,0.00003679641,0.0001243304,0.00004016624],"category_scores_gemma":[0.00001239523,0.0002111475,0.00007063654,0.0002361187,0.00001800798,0.0001551304,4.455474e-7,0.0001971245,0.000002798213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004997759,"about_ca_system_score_gemma":0.00004196275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007498124,"about_ca_topic_score_gemma":0.0004086304,"domain_scores_codex":[0.99892,0.00006072072,0.0003079206,0.0002510237,0.000282394,0.0001779287],"domain_scores_gemma":[0.999595,0.00002714548,0.00003644628,0.0001468097,0.0001271587,0.00006746832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007607337,0.0002495441,0.0001704446,0.0002076503,0.00004625232,0.000001948824,0.0001502442,0.01727294,0.8653293,0.00001322355,0.0000670353,0.1164153],"study_design_scores_gemma":[0.001068417,0.0001488821,0.000750524,0.0001094331,0.00002954341,0.000007002926,0.0001614581,0.01256784,0.9836484,0.00004494689,0.001262864,0.0002006985],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1515878,0.00001702642,0.8460004,0.0001357506,0.0004640268,0.001353928,0.00002601516,0.0002974859,0.0001175011],"genre_scores_gemma":[0.9879879,0.00006039668,0.008666998,0.0001100793,0.00002246687,0.003095664,0.000009753164,0.00003398456,0.00001278942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8373334,"threshold_uncertainty_score":0.8610346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0273730865904869,"score_gpt":0.2680685392987807,"score_spread":0.2406954527082938,"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."}}