{"id":"W4390057237","doi":"10.1002/cjce.25157","title":"Reconstruction error‐based fault detection of time series process data using generative adversarial auto‐encoders","year":2023,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Board of Research in Nuclear Sciences","keywords":"Computer science; Consistency (knowledge bases); Context (archaeology); Benchmark (surveying); Process (computing); Artificial intelligence; Time series; Data mining; Autoencoder; Machine learning; Pattern recognition (psychology); Deep learning; Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.001450055,0.0008598161,0.0007688628,0.0007855929,0.0002167364,0.0005844869,0.0009930128,0.0007512624,0.0006842887],"category_scores_gemma":[0.004822762,0.0003312243,0.0006576411,0.0004846564,0.0007454075,0.0008021352,0.0009247895,0.001475509,0.0001804658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006898166,"about_ca_system_score_gemma":0.0006653037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004667131,"about_ca_topic_score_gemma":0.002963524,"domain_scores_codex":[0.9993611,0.0001582846,0.00004128355,0.0001637867,0.0002084468,0.00006706372],"domain_scores_gemma":[0.9974092,0.001600778,0.0003251287,0.0002130386,0.0003725771,0.00007932813],"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":[0.0001150054,0.00004747347,0.002436208,0.00003904145,0.00003948532,0.0001161816,0.00004506158,0.9321826,0.003420135,0.003224172,0.0005796581,0.05775497],"study_design_scores_gemma":[7.652513e-7,0.000005568647,0.0001079545,0.00000137272,0.00000140784,0.000007855374,0.000001355364,0.998858,0.0006136968,0.0003672277,0.00003332758,0.000001540891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06834598,0.0002351509,0.9297318,0.0002295164,0.00003662585,0.00002824208,0.00006696516,0.0006501331,0.0006755432],"genre_scores_gemma":[0.9301978,0.0001468785,0.06776323,0.0001065352,0.00003205052,0.00003136623,0.0002401945,0.00004816256,0.00143376],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004667131,"threshold_uncertainty_score":0.009279907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01722542224921889,"score_gpt":0.2182647478475818,"score_spread":0.2010393255983629,"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."}}