{"id":"W3177229070","doi":"10.20965/jrm.2021.p0629","title":"Tracking and Visualizing Signs of Degradation for Early Failure Prediction of Rolling Bearings","year":2021,"lang":"en","type":"article","venue":"Journal of Robotics and Mechatronics","topic":"Gear and Bearing Dynamics Analysis","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hatch (Canada)","funders":"","keywords":"Computer science; Process (computing); Predictive maintenance; Visualization; Scheme (mathematics); Vibration; Data mining; Tracking (education); Allowance (engineering); Prognostics; Artificial intelligence; Real-time computing; Reliability engineering; Engineering","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":[0.0002553303,0.00006226472,0.0002171026,0.00009860432,0.00003021409,0.00002783844,0.00003328139,0.00005995802,7.503401e-7],"category_scores_gemma":[0.00003579981,0.00006336581,0.00008387896,0.00009002184,0.000009894474,0.000109328,0.00001389655,0.0001082055,1.634834e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001894239,"about_ca_system_score_gemma":0.00002527867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003624341,"about_ca_topic_score_gemma":0.000009301764,"domain_scores_codex":[0.999399,0.000007887991,0.0003302175,0.00005645462,0.0001172264,0.00008922655],"domain_scores_gemma":[0.9994785,0.00004449866,0.0001607241,0.00005015886,0.0002282202,0.00003786607],"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":[0.00001381304,0.00003234624,0.004197514,0.0004095541,0.0003190342,0.00000185352,0.0006150266,0.7561439,0.2270776,0.005072429,0.000007318401,0.006109663],"study_design_scores_gemma":[0.0007461582,0.0003168688,0.001994431,0.0002680469,0.0005084465,0.00003347416,0.0008105281,0.9638902,0.02960218,0.001616222,0.0001011296,0.0001123413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6191443,0.0009355185,0.3797875,0.00002575385,0.00006751826,0.00002530179,0.000006134567,0.000003705528,0.000004325],"genre_scores_gemma":[0.9633935,0.0007324743,0.03581357,0.000001524909,0.00003488089,2.018367e-7,0.0000038021,0.00001218771,0.000007868063],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3442492,"threshold_uncertainty_score":0.2583982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01356256330328816,"score_gpt":0.2198781670365236,"score_spread":0.2063156037332355,"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."}}