{"id":"W3015937655","doi":"10.1002/cjce.23760","title":"A novel data‐driven methodology for fault detection and dynamic risk assessment","year":2020,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Fault tree analysis; Event tree analysis; Computer science; Fault detection and isolation; Data mining; Naive Bayes classifier; Bayesian network; Reliability engineering; Bayes' theorem; Multivariate statistics; Classifier (UML); Bayes classifier; Process (computing); Bayesian probability; Artificial intelligence; Machine learning; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003185592,0.0000974741,0.0001933529,0.00005761991,0.00004475575,0.00004388318,0.0002212277,0.00007107911,0.00000309459],"category_scores_gemma":[0.0003441704,0.00008022282,0.0000477066,0.000088993,0.00001865184,0.00008692448,0.00001157589,0.0003501241,5.307086e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001202218,"about_ca_system_score_gemma":0.00005956807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002905046,"about_ca_topic_score_gemma":0.0005927967,"domain_scores_codex":[0.9994209,0.00001625261,0.0002335682,0.00008408965,0.00007012831,0.0001751091],"domain_scores_gemma":[0.9993238,0.0001676273,0.00005611951,0.0001131018,0.0000411589,0.0002981225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001180918,0.000001200172,0.00000981834,0.00006271942,0.0001504541,0.000003424123,0.0002097829,0.2698103,0.7213871,0.0000355578,0.00006664813,0.008251072],"study_design_scores_gemma":[0.0004442107,0.0000318877,0.00005834581,0.00001538608,0.00005223136,0.0001218527,0.00002701135,0.9892734,0.006268964,0.00001844448,0.003600445,0.00008778164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2606163,0.0005467964,0.7369451,0.0009134185,0.0006336899,0.0001876398,0.00007752146,0.00005492947,0.00002458097],"genre_scores_gemma":[0.9920442,0.000005640353,0.007652939,0.00005177408,0.0002139524,0.000005411219,0.000002059367,0.00002279828,0.000001263777],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7314278,"threshold_uncertainty_score":0.3271391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03247082375739051,"score_gpt":0.2589154145557837,"score_spread":0.2264445907983931,"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."}}