{"id":"W2124191995","doi":"10.1016/j.compchemeng.2015.09.013","title":"GMM and optimal principal components-based Bayesian method for multimode fault diagnosis","year":2015,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":97,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Agro-Industry Research and Development Special Fund of China; Fundamental Research Funds for the Central Universities; Doctoral Foundation of Shandong Province; Natural Science Foundation of Shandong Province; National Natural Science Foundation of China; Alberta Innovates - Health Solutions; Belarusian Republican Foundation for Fundamental Research; National Science Foundation","keywords":"Principal component analysis; Bayesian probability; Mixture model; Pattern recognition (psychology); Fault (geology); Bayesian inference; Artificial intelligence; Computer science; Fault detection and isolation; Inference; Gaussian process; Data mining; Gaussian","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001622996,0.0002547139,0.0003304147,0.00009214821,0.00002630122,0.0000597623,0.0001580303,0.0001313554,0.000001927898],"category_scores_gemma":[0.00005978016,0.0002753855,0.00009925491,0.0001070609,0.00001505022,0.00007955826,0.00003868605,0.0001764449,0.000003471302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000129988,"about_ca_system_score_gemma":0.00001016459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001671358,"about_ca_topic_score_gemma":4.997949e-7,"domain_scores_codex":[0.9989482,0.00001246373,0.000273099,0.0002649749,0.0001545766,0.0003466445],"domain_scores_gemma":[0.9992008,0.0002234513,0.00002402974,0.0001780793,0.00004176277,0.0003318497],"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":[0.00002305498,0.00001950374,0.00004024349,0.0001467353,0.00005750915,0.000003702468,0.0001163155,0.9504295,0.04206197,0.0000389321,0.0004450027,0.006617505],"study_design_scores_gemma":[0.001645192,0.00003142711,0.00002571759,0.0000623886,0.00001935631,0.0000117771,0.00001080172,0.9570161,0.03090588,0.000003940796,0.009965563,0.000301895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1060054,0.0002159451,0.8920879,0.0000733112,0.0006286941,0.0002917663,0.00001077797,0.0006581669,0.00002804916],"genre_scores_gemma":[0.7860773,0.000001675347,0.2134197,0.00004528776,0.0001982569,0.0001848945,0.00001563479,0.00005317358,0.000004126829],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6800718,"threshold_uncertainty_score":0.9999698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01652212242148207,"score_gpt":0.2446917988172425,"score_spread":0.2281696763957604,"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."}}