{"id":"W2601232184","doi":"10.1002/cjce.22852","title":"Combination of KPCA and causality analysis for root cause diagnosis of industrial process fault","year":2017,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Kernel principal component analysis; Process (computing); Causality (physics); Root cause; Fault (geology); Root cause analysis; Fault detection and isolation; Principal component analysis; Kernel (algebra); Nonlinear system; Computer science; Data mining; Artificial intelligence; Econometrics; Pattern recognition (psychology); Engineering; Reliability engineering; Mathematics; Kernel method; Support vector machine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0002871912,0.00008100116,0.0002902386,0.0001479724,0.00005023014,0.00003777096,0.0001929159,0.00008734434,0.000004176288],"category_scores_gemma":[0.0004500234,0.00006726152,0.0001009372,0.0001114375,0.00004437206,0.00009187414,0.00000463734,0.0001559894,5.228603e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006878027,"about_ca_system_score_gemma":0.00006258015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001228487,"about_ca_topic_score_gemma":0.001234202,"domain_scores_codex":[0.9993765,0.000007169319,0.0003323236,0.00004733286,0.0001114647,0.0001252392],"domain_scores_gemma":[0.9992989,0.00008714501,0.0001771467,0.0001417211,0.0001386344,0.000156508],"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":[0.0001610086,0.00005705155,0.1637822,0.001171654,0.006440258,0.00002071694,0.002612085,0.7209171,0.09231146,0.001216118,0.0004986246,0.01081169],"study_design_scores_gemma":[0.003293481,0.0001321249,0.01360171,0.000362801,0.001284593,0.00003845696,0.00008575902,0.5194052,0.4605688,0.0002255147,0.0006257161,0.0003757603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984522,0.0001630954,0.0008971308,0.0001396856,0.0002001928,0.00009637103,0.00002145949,0.00000612092,0.00002377661],"genre_scores_gemma":[0.9998687,0.00000220495,0.00001831315,0.000001942383,0.00008635049,0.000008689385,0.000001059019,0.00001037051,0.000002359021],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3682574,"threshold_uncertainty_score":0.2742845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02130209541132796,"score_gpt":0.2385156354674425,"score_spread":0.2172135400561145,"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."}}