{"id":"W2899446369","doi":"10.1115/detc2018-85196","title":"Roller Bearing Fault Feature Extraction Based on Compressive Sensing","year":2018,"lang":"en","type":"article","venue":"","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Compressed sensing; Matching pursuit; Feature extraction; Computer science; Pattern recognition (psychology); Bearing (navigation); Nyquist–Shannon sampling theorem; Invariant (physics); Nyquist rate; Fault (geology); Artificial intelligence; Feature (linguistics); Frequency domain; Sampling (signal processing); Rolling-element bearing; Algorithm; Computer vision; Mathematics; Acoustics; Geology","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":[0.00007452143,0.0001628713,0.0001322158,0.0001239154,0.0000791179,0.00005186915,0.00008060358,0.0001242454,0.0002065276],"category_scores_gemma":[0.00003672619,0.0001482232,0.00004815351,0.0001215902,0.0000230945,0.0001095891,0.00001629716,0.0002627223,0.00009186124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007660248,"about_ca_system_score_gemma":0.000004998454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004685599,"about_ca_topic_score_gemma":0.00004765298,"domain_scores_codex":[0.9993349,0.00001944842,0.0001093201,0.0001756141,0.0001622766,0.0001984795],"domain_scores_gemma":[0.9995103,0.00009535201,0.00002274841,0.0002603889,0.00005670745,0.00005449509],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001092207,0.0001907083,0.003051292,0.0001606797,0.0001240014,0.00007636745,0.0005032555,0.1011589,0.2156919,0.000539306,0.5315095,0.1468849],"study_design_scores_gemma":[0.0001913591,0.00005090113,0.00278447,0.00007477708,0.000007540161,0.000004340994,0.00001012083,0.7175637,0.2596637,0.00003989655,0.01942671,0.0001823804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2900904,0.00005770146,0.2829076,0.001065584,0.001140272,0.000718773,0.00001089121,0.0070427,0.4169661],"genre_scores_gemma":[0.9718388,0.000004070484,0.02711711,0.0003757524,0.0003047802,0.000007338016,0.000008852044,0.00004225584,0.000301056],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6817484,"threshold_uncertainty_score":0.6044368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009206990225033584,"score_gpt":0.2830933426987596,"score_spread":0.2738863524737261,"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."}}