{"id":"W4399772896","doi":"10.1115/1.4065777","title":"An Enhanced Modeling Framework for Bearing Fault Simulation and Machine Learning-Based Identification With Bayesian-Optimized Hyperparameter Tuning","year":2024,"lang":"en","type":"article","venue":"Journal of Computing and Information Science in Engineering","topic":"Gear and Bearing Dynamics Analysis","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hyperparameter; Machine learning; Bearing (navigation); Artificial intelligence; Computer science; Bayesian probability; Identification (biology); Bayesian optimization; Fault (geology); Bayesian inference; Pattern recognition (psychology)","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":[0.0009796715,0.00008975824,0.0001392008,0.0007170764,0.00008846258,0.0004848579,0.00008114993,0.00003910862,5.183657e-7],"category_scores_gemma":[0.0001614519,0.00007917936,0.00002520708,0.0004824826,0.00001863594,0.00202338,0.000009829141,0.0002494024,1.713745e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005186268,"about_ca_system_score_gemma":0.00002437822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002686558,"about_ca_topic_score_gemma":3.031523e-7,"domain_scores_codex":[0.9991911,0.000006143089,0.000386709,0.00008005134,0.0001944363,0.0001415666],"domain_scores_gemma":[0.9995261,0.0001588087,0.00006992238,0.00005632981,0.000131168,0.00005767307],"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":[0.000006459697,0.00000191628,0.00009848369,0.0001030362,0.000006871277,2.729674e-7,0.001003875,0.9886373,0.00154336,0.00007973088,2.708541e-8,0.008518684],"study_design_scores_gemma":[0.0001792241,0.00004426104,0.0002779829,0.0003312906,0.00001450323,0.000006638923,0.0001393872,0.9984719,0.0003935983,0.00003699356,0.000006753052,0.0000974463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4384729,0.0000440335,0.5613321,0.000007270011,0.00007081358,0.00003253679,3.008289e-7,0.00003648059,0.000003614226],"genre_scores_gemma":[0.9162453,0.00001631501,0.08369532,0.000005129619,0.00002573767,9.467975e-7,0.000002303382,0.000008616743,3.043236e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4777725,"threshold_uncertainty_score":0.4675496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009209858405910024,"score_gpt":0.2561731403148914,"score_spread":0.2469632819089814,"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."}}