{"id":"W2169335909","doi":"10.1109/acc.2013.6580040","title":"Fault-Tolerant Control design for a large off-shore wind turbine using Fuzzy Gain-Scheduling and Signal Correction","year":2013,"lang":"en","type":"article","venue":"","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Turbine; Robustness (evolution); Computer science; Fuzzy logic; Benchmark (surveying); Fault detection and isolation; Fault tolerance; Control theory (sociology); Fuzzy control system; Control engineering; Gain scheduling; Scheduling (production processes); Engineering; Real-time computing; Control (management); Artificial intelligence; Distributed computing; Actuator","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00033739,0.0005470142,0.0003558958,0.0002004331,0.000410158,0.0004663606,0.0006607802,0.0005250596,0.0006335147],"category_scores_gemma":[0.0006727832,0.0001491452,0.0002449118,0.0001715527,0.0004106672,0.000369096,0.0003111986,0.0005035026,0.0001153036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004276178,"about_ca_system_score_gemma":0.0005021584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002648846,"about_ca_topic_score_gemma":0.002604237,"domain_scores_codex":[0.9997919,0.00003175131,0.0000135487,0.00006605875,0.00007403918,0.00002264918],"domain_scores_gemma":[0.9997088,0.00007667701,0.00008563774,0.00003214154,0.00007990639,0.00001675273],"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":[0.0003064777,0.0001273159,0.001065997,0.0002063666,0.00004516212,0.0002715962,0.000164128,0.7579248,0.0805574,0.007710738,0.001184838,0.1504352],"study_design_scores_gemma":[0.00002270336,0.0001986289,0.0002862966,0.000004293384,0.000009729122,0.00003342258,0.000008176304,0.9944302,0.003873724,0.0006004131,0.0005267613,0.000005646772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06150246,0.0001142498,0.9356689,0.0001515369,0.00008132643,0.00008089256,0.00002317087,0.0003269089,0.002050451],"genre_scores_gemma":[0.9623798,0.00003968158,0.03693497,0.00003263974,0.00002625044,0.00004541123,0.00001724979,0.000005745614,0.0005183293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002648846,"threshold_uncertainty_score":0.005266845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01388365485316864,"score_gpt":0.2204472778007477,"score_spread":0.2065636229475791,"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."}}