{"id":"W4254890640","doi":"10.32920/ryerson.14656542.v1","title":"An Application of a Hybrid Wavelet-SVM Based Approach for Induction Motor Fault Detection","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Support vector machine; Artificial intelligence; Pattern recognition (psychology); Computer science; Classifier (UML); Statistical learning theory; Feature extraction; Machine learning; Fault detection and isolation; Actuator","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002753994,0.0003129976,0.0003936337,0.0002719476,0.00003503836,0.00005755244,0.0002627647,0.0003092535,0.00001621117],"category_scores_gemma":[0.00004905167,0.0003470233,0.000189474,0.0001345309,0.00001983218,0.0001278146,0.00005946003,0.0004153655,6.365684e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001939307,"about_ca_system_score_gemma":0.00003729872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002769266,"about_ca_topic_score_gemma":0.00003339752,"domain_scores_codex":[0.9985843,0.00004492867,0.0004534755,0.0005249324,0.0002043831,0.0001880217],"domain_scores_gemma":[0.9987257,0.00004338923,0.0001433018,0.0008018878,0.0002187284,0.00006704708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000414606,0.0005484275,0.0001404764,0.004022805,0.0001382766,6.345775e-7,0.00008369296,0.2676212,0.490767,0.0001033632,0.0004076372,0.236125],"study_design_scores_gemma":[0.0001021068,0.00004225687,0.0001709634,0.00002149204,0.00003008492,0.000001709537,0.00001667854,0.5606307,0.4385102,0.00009314468,0.0001915868,0.0001891108],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1406997,0.00004421614,0.8555239,0.00001091276,0.0001679586,0.001820043,0.00008423861,0.001210178,0.0004388915],"genre_scores_gemma":[0.7758135,0.00001945353,0.2189952,0.00001667833,0.0001565075,0.003739001,0.001176435,0.00007629648,0.000007009549],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6365287,"threshold_uncertainty_score":0.9998982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01264923190915563,"score_gpt":0.2741182969377594,"score_spread":0.2614690650286037,"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."}}