{"id":"W2068690150","doi":"10.1109/tie.2014.2375253","title":"Bayesian Control Loop Diagnosis by Combining Historical Data and Process Knowledge of Fault Signatures","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"Computer science; Data mining; Process (computing); Machine learning; Bayesian probability; Artificial intelligence; Fault (geology); Statistical process control; Medical diagnosis; Fault detection and isolation; Abnormality; Control (management); Process control","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":[0.001411274,0.000597893,0.0005507373,0.001178615,0.0002925517,0.000860758,0.0008084544,0.0006890965,0.0008847826],"category_scores_gemma":[0.006342118,0.000346786,0.0003448751,0.0005415389,0.0005092441,0.001626613,0.0005881753,0.0007619584,0.0001914873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007605188,"about_ca_system_score_gemma":0.00105073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008730998,"about_ca_topic_score_gemma":0.01041962,"domain_scores_codex":[0.999396,0.0001189326,0.00004012737,0.000140701,0.0002563871,0.00004782708],"domain_scores_gemma":[0.9984972,0.0007712084,0.0002380445,0.00009979201,0.0003572701,0.00003642324],"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":[0.0002118816,0.0001700151,0.005919816,0.0000992914,0.00006120859,0.0001083527,0.0001331243,0.7625959,0.004721494,0.009998825,0.001091343,0.2148888],"study_design_scores_gemma":[0.000008890177,0.00003852712,0.0008220603,0.000009047986,0.00001087039,0.00002538689,0.00001029053,0.9931307,0.001089055,0.004462535,0.000383443,0.000009181046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04488497,0.0002044302,0.9519202,0.0002312152,0.00002647155,0.0000503489,0.00007517462,0.0005120945,0.002095207],"genre_scores_gemma":[0.8527769,0.0001991733,0.1450564,0.00008446835,0.00003412416,0.0000585252,0.0002515135,0.00003578754,0.001503029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008730998,"threshold_uncertainty_score":0.01736039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01639013183971505,"score_gpt":0.2394866255467818,"score_spread":0.2230964937070667,"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."}}