{"id":"W4401632585","doi":"10.22215/etd/2023-16010","title":"Industrial Scalable Rolling Element Bearing Diagnostic and Prognostic Modelling","year":2023,"lang":"en","type":"dissertation","venue":"","topic":"Gear and Bearing Dynamics Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bearing (navigation); Engineering; Rolling-element bearing; Structural engineering; Main bearing; Finite element method; Component (thermodynamics); Roller bearing; Mechanical engineering; Computer science; Vibration; Artificial intelligence; Lubrication","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":[0.0004175912,0.0005735912,0.0005735609,0.000363743,0.0002313553,0.001060363,0.001060463,0.0007878919,0.005126814],"category_scores_gemma":[0.001116307,0.0003463487,0.0005307083,0.0003479137,0.0003416605,0.0007680913,0.000759949,0.0007086087,0.001531874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005634457,"about_ca_system_score_gemma":0.0006846103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004415017,"about_ca_topic_score_gemma":0.002981475,"domain_scores_codex":[0.9997596,0.00002588246,0.00001130544,0.00005474135,0.0001210213,0.0000274191],"domain_scores_gemma":[0.9997059,0.0000896958,0.00003371396,0.00005571772,0.00009773059,0.00001723147],"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":[0.00004118739,0.00002629249,0.0006099148,0.00007714929,0.000009215479,0.00006841758,0.00003299173,0.9545465,0.004354413,0.006432975,0.001502951,0.03229793],"study_design_scores_gemma":[0.000003073118,0.00001503132,0.0001396087,0.000004835048,0.000002362719,0.00001661323,0.000004240757,0.9956273,0.0009982844,0.001506996,0.0016782,0.000003440226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02557823,0.0004090669,0.9554835,0.0002141456,0.00007711146,0.0001177095,0.0008573376,0.002803855,0.0144591],"genre_scores_gemma":[0.784245,0.0007841255,0.1964994,0.00006390121,0.00005736454,0.0002363861,0.001710207,0.0002009369,0.01620262],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005126814,"threshold_uncertainty_score":0.01715088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0212147446360889,"score_gpt":0.2204174045450166,"score_spread":0.1992026599089277,"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."}}