{"id":"W3197391102","doi":"10.1016/j.psep.2021.08.022","title":"A deep learning model for process fault prognosis","year":2021,"lang":"en","type":"article","venue":"Process Safety and Environmental Protection","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":186,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Excellence Research Chairs, Government of Canada","keywords":"Fault (geology); Fault detection and isolation; Process (computing); Artificial intelligence; Convolutional neural network; Computer science; Machine learning; Deep learning; Artificial neural network; Recurrent neural network; Multivariate statistics; Reliability (semiconductor); Data mining; Reliability engineering; Engineering; Power (physics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00009063803,0.0001527344,0.0001511703,0.00003345323,0.0002643355,0.00003985606,0.00003778971,0.0001092141,0.00002221382],"category_scores_gemma":[0.00001777188,0.0001612917,0.00004828434,0.00008758666,0.00002468896,0.0002194544,0.00001058898,0.0001872343,0.000006555074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007167165,"about_ca_system_score_gemma":0.000006805477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002191692,"about_ca_topic_score_gemma":0.00001049285,"domain_scores_codex":[0.9991987,0.00001726913,0.0002022283,0.0002523993,0.0001304175,0.0001989398],"domain_scores_gemma":[0.9998162,0.000008771084,0.00003884184,0.00006007179,0.00001246154,0.00006363088],"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":[0.0001017026,0.0000353676,0.0001144488,0.000542675,0.00003483309,7.493439e-7,0.0007872778,0.9277108,0.007581294,0.000006190153,8.505115e-7,0.06308376],"study_design_scores_gemma":[0.0006450767,0.00005469322,0.00007972347,0.00003219737,0.00002125279,0.00003505813,0.001234103,0.989505,0.007035071,0.0002824889,0.000877086,0.0001982776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08388369,0.0009697307,0.9130667,0.00008776134,0.0001222225,0.0009478616,0.0000113664,0.0003685444,0.0005420703],"genre_scores_gemma":[0.9983637,0.0002124752,0.000108889,0.00001952517,0.00006144951,0.0009034855,0.00002671128,0.00003545821,0.0002683336],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.91448,"threshold_uncertainty_score":0.6577284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008064371891563319,"score_gpt":0.1990131390624914,"score_spread":0.190948767170928,"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."}}