{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003796967,0.001107619,0.00121752,0.002647891,0.000499278,0.001929182,0.002188523,0.0008601479,0.002313382],"category_scores_gemma":[0.01049949,0.0006513362,0.001515928,0.001405953,0.0008520794,0.00258301,0.001539524,0.001627992,0.0005838283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001109433,"about_ca_system_score_gemma":0.002189822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003118824,"about_ca_topic_score_gemma":0.002625492,"domain_scores_codex":[0.9962733,0.0007318063,0.0002909928,0.0007312988,0.001825256,0.0001473943],"domain_scores_gemma":[0.9958515,0.002090483,0.0004370178,0.0004407264,0.001092682,0.0000875964],"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":[0.0001144541,0.0002244332,0.003499992,0.0003630519,0.000287897,0.000322816,0.0001868596,0.4710653,0.01320023,0.1143046,0.002434443,0.3939959],"study_design_scores_gemma":[0.00001026327,0.00003345118,0.0002697085,0.00002222522,0.00002273484,0.00008535902,0.00001359564,0.9629501,0.00287672,0.03146166,0.002232998,0.000021179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000675127,0.00002709537,0.9988542,0.00002780513,0.000007991322,0.00002947734,0.00005238802,0.0001588789,0.0001670078],"genre_scores_gemma":[0.1339628,0.0001377897,0.8637264,0.00009664994,0.00005935477,0.0003292942,0.0004425873,0.00008793618,0.001157162],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003796967,"threshold_uncertainty_score":0.02008051,"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."}}