{"id":"W4405166317","doi":"10.1016/j.anucene.2024.111092","title":"A fault diagnosis method for rotating machinery in nuclear power plants based on long short-term memory and temporal convolutional networks","year":2024,"lang":"en","type":"article","venue":"Annals of Nuclear Energy","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Term (time); Computer science; Nuclear power; Fault (geology); Power (physics); Nuclear power plant; Reliability engineering; Physics; Engineering; Nuclear physics; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002834518,0.0001635477,0.0002576745,0.0001941086,0.00004504285,0.00005470733,0.00008101648,0.0001226979,0.00005549769],"category_scores_gemma":[0.00002390807,0.000161869,0.0001120619,0.0001141794,0.00002137806,0.00009451403,0.00001648839,0.0001410474,0.000002825598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001854881,"about_ca_system_score_gemma":0.000008311747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001481862,"about_ca_topic_score_gemma":0.00008716561,"domain_scores_codex":[0.9990361,0.000064145,0.0003008042,0.0002238202,0.000141062,0.0002341116],"domain_scores_gemma":[0.999498,0.0002582663,0.00002687896,0.0001178132,0.00002402211,0.00007501324],"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":[0.0005145607,0.0001480303,0.001899948,0.0004758491,0.0003588028,0.0001227101,0.0005512409,0.7096947,0.002492347,0.004728279,0.01793404,0.2610795],"study_design_scores_gemma":[0.0002983477,0.0001269875,0.002206296,0.0002712232,0.000007206566,0.000008432501,0.0000528351,0.98352,0.00009078505,0.00003020511,0.01321967,0.0001680405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9531072,0.005134835,0.03028659,0.001150228,0.00204952,0.000638314,0.000207202,0.0009752032,0.00645084],"genre_scores_gemma":[0.9985027,0.00007952611,0.0006816452,0.0004621416,0.0001117429,0.00004291056,0.00001395376,0.00007142527,0.00003394143],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2738253,"threshold_uncertainty_score":0.6600825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02000089315351706,"score_gpt":0.2766130586738757,"score_spread":0.2566121655203586,"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."}}