{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002711063,0.000435374,0.0002916939,0.0007812056,0.0001290864,0.0002809375,0.0003373671,0.0004173561,0.0009823177],"category_scores_gemma":[0.0009620017,0.0001459522,0.0002435947,0.0005765763,0.000181178,0.0005245093,0.0003042334,0.0003623682,0.0003875975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001721976,"about_ca_system_score_gemma":0.0001657934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002382437,"about_ca_topic_score_gemma":0.0004380945,"domain_scores_codex":[0.9997641,0.00003611166,0.00001806537,0.00004651086,0.0001218319,0.00001335919],"domain_scores_gemma":[0.9997517,0.00007211923,0.00004221837,0.00003587547,0.00008728033,0.00001078824],"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":[0.000303562,0.00007783258,0.002222771,0.0002470914,0.00003087375,0.0002091262,0.0000861423,0.007414157,0.2987076,0.001857739,0.001350242,0.6874929],"study_design_scores_gemma":[0.00006065578,0.000662836,0.015647,0.0000749359,0.00006178178,0.002966258,0.0001352677,0.5572414,0.3965217,0.001717285,0.02483096,0.00008003601],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04942124,0.0007046896,0.9473816,0.0001272285,0.0001155199,0.00004034592,0.00009826624,0.0004663285,0.001644751],"genre_scores_gemma":[0.3979655,0.0006854527,0.598334,0.0001092153,0.00004560522,0.00005538045,0.0002557405,0.00004235574,0.002506784],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009823177,"threshold_uncertainty_score":0.003286183,"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."}}