{"id":"W1901740398","doi":"10.1109/cira.2001.1013191","title":"Neural network detection and identification of actuator faults in a pneumatic process control valve","year":2002,"lang":"en","type":"article","venue":"","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Pneumatic actuator; Valve actuator; Actuator; Fault detection and isolation; Artificial neural network; Process (computing); Control valves; Overshoot (microwave communication); Computer science; Pneumatic flow control; Control theory (sociology); Engineering; Pressure control; Control engineering; Leakage (economics); Artificial intelligence; Control (management); Mechanical engineering","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":[0.0005861606,0.0003483123,0.0003404414,0.0003158037,0.0001868255,0.0003032774,0.0004430546,0.0005595672,0.0003646978],"category_scores_gemma":[0.003481361,0.0001670974,0.0001399204,0.0001493012,0.0003457806,0.0004519252,0.0002349841,0.0003419757,0.00006879987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003699656,"about_ca_system_score_gemma":0.0003085436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001724007,"about_ca_topic_score_gemma":0.001267919,"domain_scores_codex":[0.9996755,0.0000618476,0.00002618041,0.00007811144,0.0001315878,0.00002670267],"domain_scores_gemma":[0.9990729,0.0004536932,0.0001493796,0.00008023963,0.0002182194,0.0000256228],"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":[0.001399418,0.0002996253,0.01075173,0.000268621,0.000065649,0.0004080954,0.000188399,0.3481778,0.3117505,0.001966463,0.0005934713,0.3241301],"study_design_scores_gemma":[0.00001465792,0.0001693117,0.004688235,0.000007220572,0.00001196179,0.00008619533,0.00001190541,0.9318758,0.06242209,0.0005289386,0.0001707961,0.0000130501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5829717,0.0002356763,0.4142751,0.0001435725,0.00004755372,0.00006043375,0.00005382553,0.001056869,0.001155324],"genre_scores_gemma":[0.9689779,0.00003888256,0.03041837,0.00001715141,0.000005450939,0.00001982332,0.00003253283,0.000007460902,0.0004825112],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001724007,"threshold_uncertainty_score":0.003427923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006478884302687772,"score_gpt":0.2018847126975966,"score_spread":0.1954058283949088,"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."}}