{"id":"W2586354808","doi":"10.1016/j.isatra.2017.01.029","title":"A KPI-based process monitoring and fault detection framework for large-scale processes","year":2017,"lang":"en","type":"article","venue":"ISA Transactions","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"State Key Laboratory of Mechanical System and Vibration; Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Partial least squares regression; Fault detection and isolation; Process (computing); Benchmark (surveying); Kernel (algebra); Performance indicator; Scale (ratio); Representation (politics); Fault (geology); Computer science; Variable (mathematics); Engineering; Mathematics; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001557576,0.001405635,0.001484549,0.001746099,0.0008272624,0.002517499,0.002682422,0.0009359951,0.002492376],"category_scores_gemma":[0.003227006,0.0005765148,0.001187962,0.001376877,0.0008277024,0.002753469,0.002200907,0.002495524,0.00108778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001241545,"about_ca_system_score_gemma":0.003291116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01109137,"about_ca_topic_score_gemma":0.01226082,"domain_scores_codex":[0.9985903,0.0001562202,0.0000983324,0.0002710569,0.0007378768,0.0001463058],"domain_scores_gemma":[0.9987549,0.0002818209,0.0001772062,0.0002623953,0.0004125071,0.0001112705],"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":[0.0003751465,0.0005449666,0.003740442,0.0004859702,0.0002511299,0.0005597567,0.0003741525,0.4972708,0.02431129,0.1116425,0.01033109,0.3501128],"study_design_scores_gemma":[0.00001352754,0.0000327472,0.0002251012,0.00001148561,0.00002488205,0.00005499139,0.00001809186,0.9803801,0.002960074,0.01363559,0.002624556,0.00001901387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002121235,0.0001098887,0.9933403,0.00008954674,0.00001926115,0.00006471016,0.0001239575,0.003472857,0.000658301],"genre_scores_gemma":[0.3011769,0.0003367176,0.6951326,0.0001210714,0.00008461435,0.0002164692,0.0006308742,0.0003027049,0.001998075],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01109137,"threshold_uncertainty_score":0.02205366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01339415524855479,"score_gpt":0.2726770774415563,"score_spread":0.2592829221930015,"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."}}