{"id":"W2153872588","doi":"10.1109/isie.2010.5637963","title":"Rotor fault detection in induction motors using the fast orthogonal search algorithm","year":2010,"lang":"en","type":"article","venue":"","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Rotor (electric); Stator; Fast Fourier transform; Induction motor; Fault (geology); Computer science; Fault detection and isolation; Squirrel-cage rotor; Sampling (signal processing); Control theory (sociology); Condition monitoring; Transient (computer programming); Acceleration; Aliasing; Algorithm; Engineering; Voltage; Undersampling; Electrical engineering; Actuator; Physics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005450433,0.0003714363,0.0004357571,0.0009142495,0.000210879,0.0003486006,0.0002803446,0.0004699409,0.000667469],"category_scores_gemma":[0.00220293,0.0001848024,0.0002014261,0.0005089898,0.0002488986,0.0007615424,0.000287879,0.0002936042,0.0002323529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000211706,"about_ca_system_score_gemma":0.0003696471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001080531,"about_ca_topic_score_gemma":0.0009646855,"domain_scores_codex":[0.9997147,0.00009152015,0.00001992835,0.00003163307,0.0001193886,0.00002283232],"domain_scores_gemma":[0.9994222,0.0003220754,0.00008466379,0.0000376413,0.0001165716,0.00001693687],"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":[0.0005202434,0.0001239768,0.002176046,0.0001412798,0.00004825257,0.0001360934,0.0001282357,0.1129515,0.07423773,0.0103661,0.001119941,0.7980506],"study_design_scores_gemma":[0.00002379082,0.0001007195,0.000850862,0.000006731372,0.00000821318,0.0001046128,0.00001417755,0.983151,0.01295672,0.001801182,0.0009724185,0.000009684692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03891457,0.000220105,0.9597309,0.00004650174,0.00001566409,0.0000244225,0.0000153187,0.0003795113,0.0006530847],"genre_scores_gemma":[0.32997,0.0002224741,0.6682863,0.00002449484,0.00001878893,0.00005948946,0.00008780334,0.00003829952,0.00129227],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001080531,"threshold_uncertainty_score":0.002882481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0121331552917886,"score_gpt":0.2855665280085278,"score_spread":0.2734333727167392,"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."}}