{"id":"W2625410845","doi":"10.1002/cjce.22920","title":"Covariance eigenpairs neighbour distance for fault detection in chemical processes","year":2017,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Government of Shandong Province; National Natural Science Foundation of China","keywords":"Covariance; Principal component analysis; Covariance matrix; Fault detection and isolation; Eigenvalues and eigenvectors; Control limits; Statistical process control; Mathematics; Pattern recognition (psychology); Computer science; Process (computing); Control chart; Algorithm; Artificial intelligence; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001061338,0.0006513356,0.0009284057,0.002263549,0.0005543501,0.0008911344,0.001110588,0.0007276637,0.00112568],"category_scores_gemma":[0.005198495,0.0003268468,0.0008009608,0.001676999,0.0006123369,0.001050615,0.0009444795,0.0007295652,0.0004843444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006928005,"about_ca_system_score_gemma":0.0008451081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00429077,"about_ca_topic_score_gemma":0.002944412,"domain_scores_codex":[0.9986582,0.000300171,0.00008087734,0.0003246377,0.0005513512,0.00008487592],"domain_scores_gemma":[0.9981869,0.0007761026,0.0002101691,0.0002193621,0.0005388237,0.00006866249],"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":[0.0004858629,0.0001765571,0.007842687,0.0002188495,0.0002304318,0.0001716176,0.0001980862,0.4058243,0.01964057,0.01336616,0.002489126,0.5493557],"study_design_scores_gemma":[0.000007358199,0.00003610886,0.001633635,0.000005531128,0.000009826602,0.00005798229,0.00001816156,0.9895863,0.003887064,0.003921073,0.0008158855,0.0000211231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03566086,0.0003682476,0.9622269,0.0000613041,0.00005806144,0.00004229457,0.0001153299,0.0005687936,0.0008983766],"genre_scores_gemma":[0.6179931,0.0002216443,0.3794863,0.00004855334,0.00005379662,0.0001067678,0.0005010259,0.0001075184,0.001481377],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00429077,"threshold_uncertainty_score":0.00853157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007851991513469199,"score_gpt":0.1989715704100924,"score_spread":0.1911195788966232,"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."}}