{"id":"W2808510212","doi":"10.1021/acs.iecr.8b01110","title":"Fault Detection and Classification for Nonlinear Chemical Processes using Lasso and Gaussian Process","year":2018,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"Division of Civil, Mechanical and Manufacturing Innovation; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Fault detection and isolation; Interpretability; Gaussian process; Curse of dimensionality; Chemical process; Nonlinear system; Surrogate model; Algorithm; Data mining; Gaussian; Artificial intelligence; Machine learning; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00346656,0.001171988,0.001338172,0.00100135,0.0005156983,0.001020342,0.001251517,0.001170497,0.0007243335],"category_scores_gemma":[0.006477947,0.0004440812,0.00126994,0.0009009878,0.001064239,0.0009882541,0.001263705,0.001941518,0.0002098969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006797593,"about_ca_system_score_gemma":0.0008181437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002458952,"about_ca_topic_score_gemma":0.001743658,"domain_scores_codex":[0.9986305,0.000680535,0.00008562837,0.0002364618,0.000269596,0.00009722279],"domain_scores_gemma":[0.9959817,0.002712979,0.0006243779,0.0002208348,0.0003674697,0.00009262715],"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":[0.0001205696,0.00009038368,0.001675287,0.0001281714,0.0001120723,0.00009015925,0.00009480371,0.8828896,0.003713613,0.01158887,0.00108965,0.09840676],"study_design_scores_gemma":[0.000002264424,0.000008591086,0.0000812223,0.000001671285,0.000002196261,0.000005203437,0.000002041297,0.9981516,0.000242213,0.001411906,0.00008846656,0.000002573263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007726601,0.0001261278,0.9914542,0.0001563717,0.00001430359,0.00001499764,0.00003192331,0.0001982731,0.0002771838],"genre_scores_gemma":[0.551512,0.0003438856,0.4454199,0.0002737173,0.000165737,0.0002376759,0.0004276567,0.0001166392,0.001502874],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00346656,"threshold_uncertainty_score":0.01833314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08297830686761846,"score_gpt":0.3419285440671836,"score_spread":0.2589502371995651,"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."}}