{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013371,0.0007160225,0.0009794972,0.0006685784,0.0003451087,0.000812832,0.001267424,0.001074296,0.001575524],"category_scores_gemma":[0.003109001,0.0005683475,0.0008257945,0.0004272922,0.0006462264,0.0007037223,0.001130905,0.001247038,0.0004450033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005918217,"about_ca_system_score_gemma":0.001227383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005311046,"about_ca_topic_score_gemma":0.003720043,"domain_scores_codex":[0.9995135,0.000194022,0.00002268517,0.00007860862,0.0001439376,0.00004729659],"domain_scores_gemma":[0.9991983,0.0004114329,0.0001151188,0.00007554436,0.0001654954,0.00003423072],"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":[0.00001231082,0.00001499333,0.0001991023,0.00001298252,0.00001189229,0.00001924831,0.00001270838,0.9867811,0.00100253,0.005319929,0.0001619542,0.006451228],"study_design_scores_gemma":[8.579268e-7,0.000002168191,0.00001678056,8.545576e-7,7.418743e-7,0.000001479425,4.698577e-7,0.9990031,0.00008072192,0.000814661,0.00007717554,9.935519e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003302962,0.00004568686,0.9959564,0.00004513716,0.000006549626,0.00001380889,0.0000182435,0.0001902289,0.0004209269],"genre_scores_gemma":[0.6601464,0.0001788678,0.3364975,0.0001072227,0.00007167883,0.0003038667,0.0002168718,0.0001738453,0.002303786],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005311046,"threshold_uncertainty_score":0.01056027,"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."}}