{"id":"W2896695022","doi":"10.1109/tie.2018.2875644","title":"A Symmetrical Component Feature Extraction Method for Fault Detection in Induction Machines","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Feature extraction; Fault detection and isolation; Computer science; Frequency domain; Generalizability theory; Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Time domain; Automation; Engineering; Actuator; Computer vision","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":[0.0003370585,0.000598644,0.0005702725,0.001194622,0.000363223,0.0003711541,0.0004809632,0.0005345176,0.001985909],"category_scores_gemma":[0.001185988,0.0001890772,0.0004914951,0.0009695916,0.0002607256,0.0005567179,0.0002726234,0.0004790485,0.0008736236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003098191,"about_ca_system_score_gemma":0.000495108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001636353,"about_ca_topic_score_gemma":0.001575133,"domain_scores_codex":[0.9997112,0.00003551143,0.0000276634,0.0000653913,0.0001368407,0.00002339408],"domain_scores_gemma":[0.9996462,0.0001049831,0.00003937194,0.00003862861,0.0001572029,0.00001374784],"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":[0.0001304128,0.00005508235,0.0006953442,0.0001184305,0.00002285944,0.000112754,0.00004787117,0.01303326,0.09296475,0.002177856,0.002530387,0.888111],"study_design_scores_gemma":[0.00003256986,0.0002626663,0.005672385,0.000030093,0.00005569797,0.0008139439,0.00004847433,0.8825868,0.09577645,0.002669357,0.01199078,0.00006069244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00889578,0.0001635083,0.9896421,0.00004589438,0.00004095801,0.00004218105,0.00006438335,0.0006523277,0.0004528248],"genre_scores_gemma":[0.2256221,0.0003027392,0.7705998,0.00006548363,0.00004831547,0.0001382324,0.0004702782,0.00007534267,0.002677802],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001985909,"threshold_uncertainty_score":0.006643534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02489191829018658,"score_gpt":0.3225559090620946,"score_spread":0.2976639907719081,"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."}}