{"id":"W2103518422","doi":"10.1002/cjce.22221","title":"Scale‐sifting multiscale nonlinear process quality monitoring and fault detection","year":2015,"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":"Benchmark (surveying); SMA*; Computer science; Fault detection and isolation; Nonlinear system; Process (computing); Scale (ratio); Kernel (algebra); Artificial intelligence; Scale-invariant feature transform; Pattern recognition (psychology); Fault (geology); Key (lock); Data mining; Feature extraction; Algorithm; Mathematics; Actuator","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":[0.0003883239,0.0004204962,0.0004040955,0.0005614434,0.0001686749,0.0003831908,0.0004859051,0.0003937707,0.0009011797],"category_scores_gemma":[0.001190568,0.0001569656,0.0003769615,0.0005256648,0.0004017579,0.0007931163,0.0006073791,0.0003763895,0.0001661899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002879188,"about_ca_system_score_gemma":0.0003780404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001404896,"about_ca_topic_score_gemma":0.001191208,"domain_scores_codex":[0.999728,0.00002970575,0.00001357588,0.00006157812,0.0001426417,0.00002447921],"domain_scores_gemma":[0.9997216,0.00005914122,0.00006376341,0.00006046801,0.00008037868,0.00001468994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003380128,0.000111035,0.006976027,0.0002433113,0.00008885002,0.0002853918,0.0001674185,0.2378143,0.2008005,0.01701174,0.002452518,0.5337109],"study_design_scores_gemma":[0.000007134355,0.00004790261,0.001711637,0.000002994927,0.00000842108,0.00007735674,0.00001011455,0.9780936,0.01743298,0.001713653,0.0008827266,0.00001159706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05227274,0.0001160045,0.9455263,0.00009958613,0.00002673054,0.000027354,0.00005531883,0.0005334527,0.001342488],"genre_scores_gemma":[0.7440533,0.0001259339,0.2547144,0.00004094258,0.00003029423,0.00003747634,0.00009911607,0.0000482856,0.0008502027],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001404896,"threshold_uncertainty_score":0.003014803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01374962613307909,"score_gpt":0.2334213604390033,"score_spread":0.2196717343059242,"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."}}