{"id":"W2792056293","doi":"10.1109/tie.2018.2811358","title":"Recursive Slow Feature Analysis for Adaptive Monitoring of Industrial Processes","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":152,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Alberta Innovates - Technology Futures","keywords":"Feature (linguistics); Process (computing); Computer science; Property (philosophy); Rank (graph theory); Fault detection and isolation; Condition monitoring; Algorithm; Artificial intelligence; Mathematics; Engineering","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":[0.0005883015,0.0005408322,0.0005587366,0.0006360409,0.0002203797,0.0004249134,0.0005721042,0.0003863023,0.0005426795],"category_scores_gemma":[0.002736622,0.000242561,0.0003887939,0.0005848957,0.0003590791,0.0007391403,0.0004352273,0.0007372782,0.0001664519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004134757,"about_ca_system_score_gemma":0.0004664215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002174629,"about_ca_topic_score_gemma":0.001732705,"domain_scores_codex":[0.9996309,0.00009798736,0.00002261669,0.00007543033,0.0001421815,0.0000308151],"domain_scores_gemma":[0.9992542,0.0004131398,0.000106552,0.00008116706,0.0001282288,0.00001683986],"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":[0.0002381404,0.00006736862,0.002382488,0.0001451621,0.00006096437,0.0001630032,0.0001861307,0.5224665,0.05938475,0.02323962,0.001188655,0.3904772],"study_design_scores_gemma":[0.000003538876,0.00001849816,0.000298842,0.000001323821,0.000003457234,0.00001284615,0.000001992511,0.9953316,0.002117601,0.001890116,0.0003147788,0.000005453318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01028014,0.00008899256,0.9890898,0.00002736085,0.000008232667,0.00001046561,0.00001409922,0.0002846092,0.000196357],"genre_scores_gemma":[0.6933388,0.0002355918,0.3050621,0.00004096667,0.00004103236,0.0000963896,0.0001108229,0.00009957622,0.0009747937],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002174629,"threshold_uncertainty_score":0.0043239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03400049544017303,"score_gpt":0.252194090100991,"score_spread":0.2181935946608179,"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."}}