{"id":"W2014667115","doi":"10.1007/s00170-013-5222-4","title":"The use of acoustic emission information to distinguish between dry and lubricated rolling element bearings in low-speed rotating machines","year":2013,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"École de technologie supérieure","keywords":"Bearing (navigation); Acoustic emission; Lubricant; Grease; Condition monitoring; SIGNAL (programming language); Rolling-element bearing; Rotational speed; Lubrication; Engineering; Acoustics; Process (computing); Time domain; Main bearing; Mechanical engineering; Structural engineering; Automotive engineering; Materials science; Computer science; Composite material; Vibration; Artificial intelligence; Electrical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002772913,0.0001296359,0.0001892743,0.0004131975,0.00005863323,0.00008145934,0.0005721908,0.00006126262,0.000004180895],"category_scores_gemma":[0.0007853459,0.00008570972,0.00003294342,0.0001221884,0.0000459707,0.0004275796,0.0002097447,0.0003777709,0.000001247909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001048007,"about_ca_system_score_gemma":0.000008594571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000770855,"about_ca_topic_score_gemma":0.00001724658,"domain_scores_codex":[0.9988108,0.00001598195,0.0006751358,0.00007195655,0.0002557893,0.0001702828],"domain_scores_gemma":[0.9988866,0.0004072731,0.0003362318,0.0001458451,0.0001886282,0.00003543281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005419791,0.00002355285,0.03230379,0.00006039481,0.0001300296,0.000005943477,0.0005326814,0.3384337,0.05559786,0.00008938054,0.0002660321,0.5725024],"study_design_scores_gemma":[0.0009144677,0.0001664931,0.1029626,0.001009091,0.00003110807,0.00004069591,0.0003038643,0.1043876,0.7795289,0.007728764,0.002622568,0.0003037742],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885333,0.00005660987,0.009357748,0.00153438,0.0001360898,0.0002594422,0.000003953296,0.0001011543,0.0000173429],"genre_scores_gemma":[0.9879752,0.0001576174,0.01174258,0.00004542402,0.00004210862,0.00001608767,0.000002577376,0.00001384855,0.000004541389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.723931,"threshold_uncertainty_score":0.3495141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008461954294957202,"score_gpt":0.2568018135265643,"score_spread":0.2483398592316071,"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."}}