{"id":"W2789875227","doi":"10.1109/tii.2018.2815036","title":"A New Fault Prognosis of MFS System Using Integrated Extended Kalman Filter and Bayesian Method","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; University of Windsor","funders":"","keywords":"Extended Kalman filter; Kalman filter; Residual; Recursive Bayesian estimation; Bayesian probability; Fault (geology); Control theory (sociology); Path (computing); Transformation (genetics); Computer science; Condition-based maintenance; Fault detection and isolation; Prognostics; Engineering; Control system; Control engineering; Reliability engineering; Data mining; Algorithm; Artificial intelligence; Control (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":[0.0002356241,0.0002104047,0.0003449711,0.0002918986,0.0001160021,0.00007015578,0.0001046562,0.0002962156,0.0000560384],"category_scores_gemma":[0.000007841202,0.0001902325,0.0000898654,0.0004450689,0.00004137421,0.0002805163,0.000001050979,0.0003450395,0.00001416903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001281097,"about_ca_system_score_gemma":0.00006587624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002964651,"about_ca_topic_score_gemma":0.00004512608,"domain_scores_codex":[0.9986746,0.00005896578,0.000757626,0.00008771508,0.0002036942,0.000217423],"domain_scores_gemma":[0.9993729,0.00006360227,0.0001264834,0.0001969789,0.00009437282,0.0001456608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006149794,0.0001283043,0.00002594385,0.0009403558,0.00133389,0.000004065069,0.01053467,0.1880632,0.01451594,0.0002046603,0.002998981,0.780635],"study_design_scores_gemma":[0.001512734,0.0002336821,0.000002418623,0.0003066162,0.000111361,0.00004876454,0.002591634,0.9225322,0.07046045,0.000003995868,0.001988719,0.0002074433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0299124,0.000008634691,0.9666317,0.00001169586,0.001586813,0.0004667878,0.00004838538,0.0002951297,0.001038497],"genre_scores_gemma":[0.9863971,0.000003180817,0.0132333,0.00001639116,0.0001915573,0.00002449267,0.000001666846,0.00002670954,0.0001055624],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9564847,"threshold_uncertainty_score":0.7757455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03674013599787648,"score_gpt":0.2657160588144656,"score_spread":0.2289759228165892,"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."}}