{"id":"W4386509127","doi":"10.1002/cjce.25085","title":"Fault detection and diagnosis in a <scp>non‐Gaussian</scp> process with modified kernel independent component regression","year":2023,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Shanxi Provincial Education Department; National Natural Science Foundation of China","keywords":"Principal component regression; Kernel principal component analysis; Kernel (algebra); Partial least squares regression; Principal component analysis; Kernel regression; Fault detection and isolation; Independent component analysis; Computer science; Kriging; Mathematics; Regression analysis; Fault (geology); Statistics; Gaussian; Component (thermodynamics); Regression; Algorithm; Kernel method; Artificial intelligence; Support vector machine","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":[0.0011048,0.0005734944,0.0005490046,0.0007105972,0.0003636436,0.000666304,0.0005083971,0.000648381,0.000382703],"category_scores_gemma":[0.00222279,0.0001946756,0.000532157,0.000721481,0.000579673,0.0005103836,0.0003753103,0.0004895182,0.00009940171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007770183,"about_ca_system_score_gemma":0.0009558221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0147583,"about_ca_topic_score_gemma":0.007529875,"domain_scores_codex":[0.9993615,0.0001462878,0.00002711748,0.0001716811,0.0002390341,0.00005438752],"domain_scores_gemma":[0.9992045,0.0003194674,0.000160341,0.00005127995,0.000245764,0.00001878839],"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":[0.0004638197,0.0002071254,0.008835245,0.0001270098,0.00008517647,0.0003902704,0.0001364421,0.8410527,0.02574023,0.003524947,0.0006786934,0.1187584],"study_design_scores_gemma":[0.000002822259,0.00001898618,0.000813651,6.698085e-7,0.000003619563,0.000007800627,0.000004190513,0.9975562,0.001380467,0.0001651178,0.00004314915,0.000003276583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.320998,0.00016021,0.6761209,0.0002440422,0.00003603379,0.00005397882,0.00004790182,0.0007983302,0.001540585],"genre_scores_gemma":[0.9711133,0.00003154424,0.02836355,0.00001273957,0.000004959977,0.000008636208,0.00002382638,0.00001213399,0.0004293061],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0147583,"threshold_uncertainty_score":0.0293448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007167033126795905,"score_gpt":0.1933774274766923,"score_spread":0.1862103943498964,"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."}}