{"id":"W4384826723","doi":"10.32920/23709630","title":"Stochastic Fault Diagnosis using a Generalized Polynomial Chaos Model and Maximum Likelihood","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Fault (geology); Variable (mathematics); Particle filter; Polynomial chaos; Polynomial; Inverse; Applied mathematics; Maximum likelihood; Filter (signal processing); Algorithm; Fault detection and isolation; Estimation theory; Fraction (chemistry); Stochastic process; Control theory (sociology); Statistics; Computer science; Kalman filter; Artificial intelligence; Monte Carlo method; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0006463616,0.0004405542,0.0006816793,0.0005910559,0.0002808467,0.000638802,0.000611282,0.0007432813,0.0006924097],"category_scores_gemma":[0.002694811,0.0003436428,0.000643832,0.0005452712,0.0007122183,0.0007828145,0.0007248842,0.0008355591,0.000164359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007399801,"about_ca_system_score_gemma":0.0008351376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005074461,"about_ca_topic_score_gemma":0.002951392,"domain_scores_codex":[0.9996101,0.0001320884,0.00001616478,0.00006383437,0.0001465384,0.00003124258],"domain_scores_gemma":[0.9991465,0.0005552231,0.0001124391,0.00005441976,0.0001063557,0.00002513175],"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":[0.0000545652,0.00001298599,0.0006240685,0.0000440694,0.00003314945,0.00009591956,0.00005925618,0.9521906,0.00376622,0.01473292,0.0003489122,0.02803733],"study_design_scores_gemma":[0.000001821598,0.000004350127,0.00005066912,9.577914e-7,0.000001155555,0.000008518997,0.000001156809,0.9977301,0.0002979194,0.001811641,0.00008936959,0.000002403363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01292786,0.0001050874,0.9859849,0.0001708981,0.0000151847,0.00001126545,0.00002720023,0.0001505416,0.000607079],"genre_scores_gemma":[0.8296061,0.0002041818,0.167309,0.00006771163,0.00004901715,0.00005026864,0.0001019476,0.00007128478,0.00254043],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005074461,"threshold_uncertainty_score":0.01008987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03098804391028422,"score_gpt":0.2532795804207568,"score_spread":0.2222915365104726,"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."}}