{"id":"W4407307115","doi":"10.3390/machines13020125","title":"Power Transformer Prognostics and Health Management Using Machine Learning: A Review and Future Directions","year":2025,"lang":"en","type":"review","venue":"Machines","topic":"Power Transformer Diagnostics and Insulation","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec","funders":"","keywords":"Prognostics; Transformer; Reliability engineering; Computer science; Engineering; Machine learning; Electrical engineering; Voltage","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":[0.000192454,0.0004275011,0.001069203,0.0002352476,0.0001697342,0.00005375312,0.00007661597,0.0001248867,0.00002982346],"category_scores_gemma":[0.000009494178,0.0003508446,0.0001343539,0.0003835184,0.00002301309,0.00007322118,0.00001866518,0.0004009971,0.000001805976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006238989,"about_ca_system_score_gemma":0.00003489021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002513885,"about_ca_topic_score_gemma":0.00002057683,"domain_scores_codex":[0.9988221,0.00004947875,0.0004902109,0.0002747973,0.0001089116,0.000254504],"domain_scores_gemma":[0.9996138,0.00005757245,0.00006763076,0.000141026,0.00002066033,0.0000993541],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[3.018708e-7,0.0000144048,0.0000264349,0.1521456,0.0001789431,0.000002051748,0.00004046529,0.000006955144,2.041765e-8,0.0002281049,0.0001830144,0.8471737],"study_design_scores_gemma":[0.0001035601,0.0000276341,0.00006424866,0.02526948,0.001205772,0.00003028902,0.00000277052,0.0007342363,3.475366e-8,0.00002015077,0.9722505,0.0002912961],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[9.984791e-7,0.9945363,0.0007843606,0.0002675789,0.000398628,0.00141113,0.0001350791,0.000165591,0.002300356],"genre_scores_gemma":[0.000006144869,0.9986303,0.0007006782,0.0001108986,0.00007496215,0.00008694112,0.0002085122,0.00005835816,0.0001232018],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9720675,"threshold_uncertainty_score":0.9998944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01949077214620215,"score_gpt":0.3014236317047524,"score_spread":0.2819328595585502,"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."}}