{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004557775,0.0005439006,0.0006630749,0.0003641399,0.0002616152,0.0005816901,0.001271622,0.001233568,0.002331136],"category_scores_gemma":[0.00141936,0.0004194001,0.0005305973,0.0004420113,0.0003284089,0.0009272083,0.0007883786,0.001586655,0.0006312901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008616918,"about_ca_system_score_gemma":0.001074347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01495249,"about_ca_topic_score_gemma":0.01359559,"domain_scores_codex":[0.9998497,0.00001999257,0.000009140458,0.00005125037,0.00003811402,0.00003169914],"domain_scores_gemma":[0.9995503,0.0001787736,0.00004185551,0.00004781015,0.0001541282,0.00002712842],"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":[0.00009627652,0.00005936942,0.0007119056,0.000043023,0.00003818376,0.00004035136,0.00001661632,0.8958944,0.002551024,0.005115476,0.002217939,0.09321544],"study_design_scores_gemma":[0.000001223329,0.000005473222,0.00004512326,0.000001814931,0.000002767165,0.000002573346,4.966353e-7,0.9985806,0.0002191187,0.001033699,0.0001057428,0.000001355311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03395401,0.0008506998,0.9607081,0.0006076536,0.0001590943,0.00002460687,0.0004571314,0.001108914,0.002129829],"genre_scores_gemma":[0.8972016,0.000602485,0.08930507,0.0003144976,0.0001013804,0.00009679842,0.0008565586,0.00008319857,0.01143847],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01495249,"threshold_uncertainty_score":0.02973092,"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."}}