{"id":"W2248240856","doi":"10.1109/smc.2015.30","title":"Imputation of Missing Data for Diagnosing Sensor Faults in a Wind Turbine","year":2015,"lang":"en","type":"article","venue":"","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Missing data; Imputation (statistics); Computer science; Turbine; Fault detection and isolation; Data mining; Fault (geology); Condition monitoring; Data processing; Wind power; Data modeling; Data set; Set (abstract data type); Reliability engineering; Real-time computing; Engineering; Artificial intelligence; Machine learning; Database","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":[0.00354196,0.0005433443,0.0009760244,0.0008561747,0.0004340885,0.0005304564,0.001046017,0.0009949858,0.0006050016],"category_scores_gemma":[0.0124965,0.0003646653,0.0005909425,0.0007735949,0.0004632803,0.0008116153,0.000597934,0.001138498,0.0002315113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003013429,"about_ca_system_score_gemma":0.0008117008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001213834,"about_ca_topic_score_gemma":0.0016534,"domain_scores_codex":[0.9990016,0.0004257524,0.00009125187,0.0001905541,0.0002145494,0.0000762883],"domain_scores_gemma":[0.9931794,0.004213445,0.0007914476,0.0008897128,0.0007908885,0.0001350431],"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":[0.001167687,0.0002945654,0.03073451,0.0004459629,0.0002097313,0.001005431,0.0004349576,0.6620916,0.01105516,0.004647043,0.001821849,0.2860916],"study_design_scores_gemma":[0.00002481286,0.0001476947,0.004226984,0.00002917292,0.00003867302,0.0002060728,0.00006777475,0.982936,0.007334142,0.004404624,0.0005618727,0.00002220578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07503572,0.0003081585,0.9233373,0.0001708625,0.00004943304,0.00004210925,0.0002334679,0.0005552892,0.0002677363],"genre_scores_gemma":[0.8212027,0.0002255179,0.1772003,0.00006000934,0.00004951547,0.00007776813,0.0007216641,0.00003580363,0.0004266515],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00354196,"threshold_uncertainty_score":0.01873189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04177020521135146,"score_gpt":0.2831450589042019,"score_spread":0.2413748536928505,"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."}}