{"id":"W2766046803","doi":"10.1002/cjce.23051","title":"Fault prognosis technology for non‐Gaussian and nonlinear processes based on KICA reconstruction","year":2017,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Fault (geology); Nonlinear system; Independent component analysis; Kernel (algebra); Reliability (semiconductor); Computer science; Gaussian; Autocorrelation; Fault detection and isolation; Process (computing); Component (thermodynamics); Algorithm; Pattern recognition (psychology); Engineering; Data mining; Artificial intelligence; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001181188,0.0001049534,0.0001640467,0.0001603385,0.0001382124,0.0001073539,0.0002089448,0.0001096437,0.000002890381],"category_scores_gemma":[0.000362938,0.00008153066,0.00004337319,0.00007164708,0.00005798726,0.00007850833,0.000003395611,0.0002405227,8.255349e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000832976,"about_ca_system_score_gemma":0.0001105137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006439584,"about_ca_topic_score_gemma":0.0002024371,"domain_scores_codex":[0.999484,0.000002181671,0.0001901658,0.00006733713,0.000066549,0.0001897675],"domain_scores_gemma":[0.9994859,0.0000418005,0.00007271943,0.0001392746,0.0000824296,0.0001779164],"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":[0.0002266435,0.00004451805,0.003147248,0.002681765,0.0006965444,0.0000980418,0.0006306621,0.374562,0.3810232,0.0006194379,0.001447299,0.2348227],"study_design_scores_gemma":[0.0007298308,0.00007619276,0.00008871892,0.0003552062,0.0000314348,0.000201696,0.00002152617,0.8633758,0.1287379,0.00005048912,0.006160281,0.0001708986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9861402,0.0003720796,0.00659651,0.004410223,0.001295027,0.0004648833,0.00002946617,0.0001142741,0.000577316],"genre_scores_gemma":[0.998717,0.000002514064,0.0009771028,0.00001899696,0.0002352901,0.00002008978,4.145246e-7,0.00002177133,0.000006796447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4888139,"threshold_uncertainty_score":0.3324724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006208012557413304,"score_gpt":0.194718972480077,"score_spread":0.1885109599226637,"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."}}