{"id":"W2069445560","doi":"10.1109/ias.2012.6374027","title":"On line trained fuzzy logic and adaptive continuous wavelet transform based high precision fault detection of IM with broken rotor bars","year":2012,"lang":"en","type":"article","venue":"","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Universiti Malaya","keywords":"Control theory (sociology); Stator; Fault (geology); Rotor (electric); Continuous wavelet transform; Wavelet; Squirrel-cage rotor; Induction motor; Harmonics; Engineering; Fault detection and isolation; Fuzzy logic; Wavelet transform; Computer science; Artificial intelligence; Discrete wavelet transform; Actuator; Voltage; Electrical engineering","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":[0.0002742049,0.0003068852,0.0002543215,0.0002918007,0.0001361921,0.0003239549,0.0003950344,0.0003822296,0.0007351598],"category_scores_gemma":[0.001035327,0.0001181782,0.0002116253,0.0001682886,0.0001974714,0.0003702394,0.0001450652,0.0002998713,0.0001373472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002636573,"about_ca_system_score_gemma":0.0002232899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00279526,"about_ca_topic_score_gemma":0.002526273,"domain_scores_codex":[0.9997901,0.00002838222,0.00001423621,0.00004572616,0.0001018492,0.00001970831],"domain_scores_gemma":[0.9996586,0.0001156031,0.00005322737,0.00003559113,0.0001235893,0.00001337894],"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":[0.0006343178,0.0002238069,0.003544094,0.0001666483,0.00007560628,0.000367633,0.0001714757,0.2499131,0.153962,0.002600414,0.00105117,0.5872898],"study_design_scores_gemma":[0.0000150555,0.000119587,0.00127419,0.000004789937,0.00001281579,0.00006487905,0.000007811236,0.9831638,0.01473602,0.0002634605,0.0003309991,0.000006605418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1545526,0.0001418021,0.8420402,0.00007468888,0.00005165305,0.00004595357,0.00002830489,0.0006982319,0.002366545],"genre_scores_gemma":[0.9246876,0.00006785679,0.07386119,0.00003634098,0.00001309043,0.00002886899,0.00003370875,0.00001465814,0.001256618],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00279526,"threshold_uncertainty_score":0.005558014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009775068489547551,"score_gpt":0.2375438954965541,"score_spread":0.2277688270070066,"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."}}