{"id":"W4406054666","doi":"10.34218/ijmet_15_06_002","title":"AN ENHANCED TEAGER HUANG TRANSFORM TECHNIQUE FOR BEARING FAULT DETECTION","year":2024,"lang":"en","type":"article","venue":"INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY (IJMET)","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Optech (Canada); Lakehead University","funders":"","keywords":"Bearing (navigation); Fault detection and isolation; Computer science; Pattern recognition (psychology); Artificial intelligence; Speech recognition; Fault (geology); Seismology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003094769,0.0004268316,0.0004505824,0.0006281234,0.0002299869,0.0004225447,0.0004802205,0.0004984895,0.003281907],"category_scores_gemma":[0.0007789057,0.0001640553,0.0003381066,0.0006439685,0.0001674912,0.0008604447,0.0004194298,0.0006552991,0.001218809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001420819,"about_ca_system_score_gemma":0.0003216631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006520284,"about_ca_topic_score_gemma":0.001432787,"domain_scores_codex":[0.9997003,0.00005074889,0.00001137287,0.00004234369,0.0001712914,0.00002402696],"domain_scores_gemma":[0.9996477,0.0001449518,0.00002581653,0.00005164869,0.0001144807,0.00001533214],"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":[0.0002865801,0.0001109045,0.0003728861,0.0001125583,0.00004096777,0.0001047707,0.00006665732,0.00942719,0.3128081,0.00493375,0.001749394,0.6699862],"study_design_scores_gemma":[0.00003783092,0.0005212729,0.002606831,0.00001783854,0.00008628653,0.000788493,0.00003901208,0.8092771,0.1677683,0.00300969,0.01580469,0.00004251765],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0148706,0.0004700749,0.9823697,0.00007978888,0.00007845888,0.00002361908,0.00005030993,0.0006222536,0.001435083],"genre_scores_gemma":[0.2587681,0.0008360552,0.7289959,0.0001604657,0.0001976878,0.00005498384,0.000326519,0.0001496992,0.01051069],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003281907,"threshold_uncertainty_score":0.01097912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004602948778659756,"score_gpt":0.2694519473728808,"score_spread":0.264848998594221,"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."}}