{"id":"W1971663396","doi":"10.1016/j.engappai.2012.09.003","title":"A new fault diagnosis method of multimode processes using Bayesian inference based Gaussian mixture contribution decomposition","year":2012,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Mahalanobis distance; Gaussian process; Weighting; Gaussian; Inference; Pattern recognition (psychology); Principal component analysis; Bayesian inference; Bayesian probability; Artificial intelligence; Multivariate normal distribution; Data mining; Algorithm; Multivariate statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002057898,0.0001744265,0.0002552742,0.0002100605,0.0000499925,0.00001961989,0.0001682486,0.0001224915,0.00003099769],"category_scores_gemma":[0.0001439109,0.0001922053,0.00007177308,0.0007203646,0.00001942175,0.0001815663,0.00001134741,0.0001338901,0.00000789515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007467163,"about_ca_system_score_gemma":0.00004477026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002008302,"about_ca_topic_score_gemma":0.0000206771,"domain_scores_codex":[0.9988854,0.00002287556,0.0005317166,0.0001480595,0.0001678319,0.0002440964],"domain_scores_gemma":[0.9990785,0.0002533575,0.0001168784,0.0002483959,0.0001710863,0.0001317719],"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":[0.000009163842,0.00006216145,0.0002533593,0.000231502,0.00002956314,7.372163e-8,0.0001991398,0.8253824,0.1361554,0.002509127,0.000006199541,0.0351619],"study_design_scores_gemma":[0.00002576001,0.00001173538,0.0000680176,0.00007473359,0.00002348754,0.000001636205,0.00003897098,0.5935968,0.4056629,0.0001003472,0.000282546,0.0001131315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008792822,0.0004985467,0.9898417,0.00002389205,0.0001486229,0.0004423713,0.00002935953,0.0001981068,0.00002455233],"genre_scores_gemma":[0.8814015,0.00001381548,0.1182069,0.000003836244,0.0001247416,0.0002108402,0.00001351536,0.00002323355,0.00000166453],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8726087,"threshold_uncertainty_score":0.7837903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01809231811778398,"score_gpt":0.3114374573155255,"score_spread":0.2933451391977415,"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."}}