{"id":"W4405802074","doi":"10.1016/j.measurement.2024.116589","title":"Feature learning for bearing prognostics: A comprehensive review of machine/deep learning methods, challenges, and opportunities","year":2024,"lang":"en","type":"review","venue":"Measurement","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fondation familiale Trottier","keywords":"Prognostics; Artificial intelligence; Machine learning; Bearing (navigation); Feature (linguistics); Computer science; Deep learning; Feature learning; Engineering; Pattern recognition (psychology); Reliability engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002397099,0.0007847121,0.002557719,0.0003429908,0.00007661931,0.00005153666,0.0002823354,0.0003154711,0.000009910873],"category_scores_gemma":[0.0007939285,0.0006638466,0.0005273064,0.0001347073,0.00004615807,0.00006280333,0.0001799274,0.001418365,0.00000343303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002357437,"about_ca_system_score_gemma":0.00007255653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005464423,"about_ca_topic_score_gemma":0.000005367443,"domain_scores_codex":[0.9971763,0.0005581919,0.0007912559,0.0005231926,0.0005556264,0.0003954651],"domain_scores_gemma":[0.9984028,0.0004671743,0.0003125162,0.0003395517,0.0003538063,0.0001240917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[6.103703e-7,0.000008814253,2.074511e-7,0.417877,0.0003671885,0.000004662174,0.00007799941,0.000009284756,0.000001901021,0.00009297571,0.001144029,0.5804153],"study_design_scores_gemma":[0.00006046438,0.00009805262,6.607281e-7,0.2519443,0.002060656,0.0000238977,0.00003137604,0.0007783136,0.000009329515,0.00002638314,0.7445528,0.0004137546],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[4.249347e-8,0.9916619,0.003493547,0.0001330078,0.0002378708,0.002729332,0.00002481313,0.0007516538,0.0009678542],"genre_scores_gemma":[0.000002374251,0.9799395,0.01777824,0.00002765657,0.0001337627,0.001648867,0.0001522932,0.0002472029,0.00007015605],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7434087,"threshold_uncertainty_score":0.9995813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2138713331037741,"score_gpt":0.3964807461656869,"score_spread":0.1826094130619128,"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."}}