{"id":"W2044799367","doi":"10.1016/j.jprocont.2012.02.012","title":"A particle filter driven dynamic Gaussian mixture model approach for complex process monitoring and fault diagnosis","year":2012,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":104,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Particle filter; Fault detection and isolation; Mixture model; Gaussian process; Bayesian inference; Fault (geology); Gaussian; Inference; Computer science; Principal component analysis; Bayesian probability; Gaussian filter; Algorithm; Dynamic Bayesian network; Pattern recognition (psychology); Artificial intelligence; Kalman filter","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":[0.0002273748,0.0001846209,0.0003882554,0.00007792877,0.00008928953,0.00007587997,0.0001445969,0.0001009156,0.000004423894],"category_scores_gemma":[0.00005358591,0.000148504,0.0001065178,0.0001023735,0.00002137246,0.0005054981,0.0000053837,0.0002144196,9.890764e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005507944,"about_ca_system_score_gemma":0.00001979153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.567989e-7,"about_ca_topic_score_gemma":0.000001038529,"domain_scores_codex":[0.9988465,0.00002393261,0.0004318312,0.0001073426,0.0002268669,0.0003634906],"domain_scores_gemma":[0.999283,0.00005854067,0.0001730405,0.0000881496,0.0001752985,0.0002219311],"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":[0.0004620742,0.0003313865,0.04516861,0.00186922,0.0006466145,0.000004459204,0.005643619,0.8971291,0.03416109,0.0000453373,0.0003248401,0.01421363],"study_design_scores_gemma":[0.002657845,0.00007453909,0.001426397,0.00006948971,0.0001082356,0.00005963487,0.0005926649,0.9929157,0.001683085,0.00006111294,0.0001707752,0.0001805385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7004494,0.003484542,0.294544,0.0002298977,0.0003820457,0.0006420675,0.00002817008,0.00009937045,0.0001405141],"genre_scores_gemma":[0.9980164,0.00003555661,0.001152486,0.00003703294,0.0004343002,0.0002588614,0.0000010693,0.00003936488,0.00002492947],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.297567,"threshold_uncertainty_score":0.6055818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01762840553749966,"score_gpt":0.2681752496482049,"score_spread":0.2505468441107053,"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."}}