{"id":"W4414871431","doi":"10.1109/access.2025.3618193","title":"Fault Detection of Three-Phase Controlled Rectifiers Using Supervised Machine Learning Algorithms","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Bangladesh University of Engineering and Technology","keywords":"Fault detection and isolation; Random forest; Reliability (semiconductor); Fault (geology); Support vector machine; Power (physics); Naive Bayes classifier; Statistical classification; Supervised learning","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":[0.0005149809,0.0004133772,0.0004191689,0.0006175001,0.0001572102,0.000377511,0.0004213604,0.0004015748,0.0004793308],"category_scores_gemma":[0.002045347,0.0001223961,0.0003560574,0.0003190722,0.0001636046,0.0003443811,0.0001961226,0.000321159,0.0001797164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002883655,"about_ca_system_score_gemma":0.0003972835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002205746,"about_ca_topic_score_gemma":0.002538846,"domain_scores_codex":[0.9996487,0.00009139862,0.00003476877,0.00007972933,0.0001150692,0.00003025838],"domain_scores_gemma":[0.9988075,0.0006154762,0.0001777892,0.0001106617,0.0002706677,0.00001791405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004916856,0.0003544886,0.01246301,0.0002045494,0.00009758166,0.0001906546,0.0001014931,0.5615805,0.02597628,0.001294071,0.001630047,0.3956157],"study_design_scores_gemma":[0.000006219318,0.00005345987,0.001583122,0.000004318337,0.000004712917,0.00003034063,0.000009807829,0.9921517,0.005615149,0.0003667222,0.0001708358,0.00000362126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4665229,0.0003214425,0.5288837,0.0001131509,0.0000403415,0.00008909956,0.0002853079,0.002107037,0.001637102],"genre_scores_gemma":[0.9528379,0.0000487811,0.0462225,0.00001451867,0.000008305153,0.0000407513,0.0003467588,0.00001866312,0.0004618621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002205746,"threshold_uncertainty_score":0.004385769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0232116957924037,"score_gpt":0.2966625557416893,"score_spread":0.2734508599492856,"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."}}