{"id":"W78932006","doi":"10.3182/20100705-3-be-2011.00019","title":"Fault detection using CUSUM based techniques with application to the Tennessee Eastman Process","year":2010,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"CUSUM; Fault detection and isolation; Process (computing); Statistical process control; Fault (geology); Engineering; Reliability engineering; Computer science; Control theory (sociology); Real-time computing; Control engineering; Control (management); Artificial intelligence; Operations management","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002030944,0.0002036929,0.0001578593,0.0001402198,0.0002176602,0.0001819806,0.0002420114,0.0001252821,0.000007548011],"category_scores_gemma":[0.00003192036,0.0001466487,0.00003916078,0.0005184653,0.00003413063,0.0001952226,0.00001276826,0.000304689,0.00002904658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005440908,"about_ca_system_score_gemma":0.0000183048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009129212,"about_ca_topic_score_gemma":0.0003020514,"domain_scores_codex":[0.9990081,0.000003554841,0.0001992977,0.0002613521,0.0002639233,0.0002637532],"domain_scores_gemma":[0.9994416,0.000009591917,0.00006584522,0.0001517591,0.0002399609,0.00009126316],"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":[0.00004185321,0.00001961265,0.002140008,0.0001622822,0.00002349199,3.339533e-7,0.0003720701,0.002756213,0.9493849,0.00004107092,0.0000940096,0.04496419],"study_design_scores_gemma":[0.0001842943,0.00006577309,0.0007516157,0.00004567753,0.00002402267,0.00002756389,0.0004344833,0.6841828,0.2924127,0.00003064523,0.0215728,0.000267582],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9348682,0.00001644672,0.06125378,0.0002170392,0.0002402581,0.0009640295,0.000003275483,0.001158321,0.001278614],"genre_scores_gemma":[0.9976777,5.969158e-7,0.001078293,0.00008483139,0.0003440188,0.0006967008,9.762363e-7,0.00005727299,0.00005963878],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6814266,"threshold_uncertainty_score":0.5980158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004031052360832332,"score_gpt":0.2134297650948742,"score_spread":0.2093987127340419,"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."}}