{"id":"W2121482882","doi":"10.1088/1742-6596/305/1/012129","title":"Teager Energy Spectrum for Fault Diagnosis of Rolling Element Bearings","year":2011,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Rolling-element bearing; Energy operator; Bearing (navigation); Energy (signal processing); Vibration; Fault (geology); Structural engineering; Acoustics; Fourier transform; Computer science; Engineering; Artificial intelligence; Physics; Mathematics; Geology; Mathematical analysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003116906,0.0003080557,0.0002839164,0.001262007,0.0001419463,0.0003030078,0.0002698476,0.0004839842,0.000979194],"category_scores_gemma":[0.001288825,0.0001275161,0.0001834016,0.0004091938,0.0002603515,0.0006874039,0.0002308879,0.0002928667,0.0001814212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000157554,"about_ca_system_score_gemma":0.0001272977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004470092,"about_ca_topic_score_gemma":0.000517254,"domain_scores_codex":[0.9998272,0.0000401226,0.000008478719,0.00003142491,0.00007757331,0.00001525049],"domain_scores_gemma":[0.9994853,0.0002866724,0.0000499066,0.00005676707,0.00009423705,0.00002708927],"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":[0.0008817145,0.0001474492,0.003861008,0.0001610875,0.0000530381,0.000391826,0.0001883694,0.07749011,0.3925757,0.005333808,0.0007904757,0.5181255],"study_design_scores_gemma":[0.00002232171,0.0002267056,0.008404282,0.0000179452,0.00001892415,0.0003817173,0.00006275952,0.9258891,0.05952617,0.004216872,0.001198128,0.00003519332],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2616702,0.0006889871,0.7350234,0.0001099368,0.00005431312,0.00003258291,0.00007965267,0.0007453408,0.001595631],"genre_scores_gemma":[0.9037714,0.0002102237,0.09503421,0.00002974857,0.00002199304,0.0000146844,0.00007726134,0.00002676755,0.0008137169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001262007,"threshold_uncertainty_score":0.003275692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02476497845929168,"score_gpt":0.248063045840384,"score_spread":0.2232980673810923,"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."}}