{"id":"W2337538840","doi":"10.1177/1350650115619611","title":"Optimized statistical parameters of acoustic emission signals for monitoring of rolling element bearings","year":2015,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Qatar National Research Fund","keywords":"Acoustic emission; Rolling-element bearing; Prognostics; SIGNAL (programming language); Condition monitoring; Bearing (navigation); Signal processing; Engineering; Structural engineering; Acoustics; Computer science; Electronic engineering; Vibration; Reliability engineering; Artificial intelligence; Electrical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007364717,0.0006141154,0.0003860698,0.0003600181,0.0001305186,0.0002491097,0.000289594,0.0004114019,0.0005111682],"category_scores_gemma":[0.003215147,0.0001686972,0.0001588278,0.0002031374,0.0002692783,0.0004257466,0.0001084467,0.0002086635,0.0001207317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001329184,"about_ca_system_score_gemma":0.000150339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000133062,"about_ca_topic_score_gemma":0.0002022686,"domain_scores_codex":[0.9993827,0.0001760407,0.00004500388,0.0001300281,0.0002276256,0.00003844638],"domain_scores_gemma":[0.9970295,0.001895278,0.000391792,0.0001755316,0.000462425,0.00004543053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007993507,0.00019505,0.004407032,0.0001698127,0.00002386609,0.00006211024,0.00008715154,0.01099528,0.9450888,0.0001714234,0.00007250283,0.03792762],"study_design_scores_gemma":[0.00004885453,0.001700526,0.03398273,0.00001174687,0.0000618724,0.0002271012,0.00007800673,0.07445338,0.8886172,0.000196275,0.0005765698,0.00004567216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8710136,0.0002745279,0.1276215,0.00004217492,0.00003231728,0.00008100068,0.0001307182,0.0003139465,0.0004902529],"genre_scores_gemma":[0.9861023,0.00008401086,0.0135894,0.000008110414,0.000006630647,0.00004473474,0.00005656997,0.00001571689,0.00009254909],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0007364717,"threshold_uncertainty_score":0.003894925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0257259520178631,"score_gpt":0.283248172492525,"score_spread":0.2575222204746619,"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."}}