{"id":"W4404065481","doi":"10.1088/2631-8695/ad8f17","title":"Cepstrum-driven modulated empirical wavelet transform and its application in bearing fault diagnosis","year":2024,"lang":"en","type":"article","venue":"Engineering Research Express","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"MD Precision (Canada)","funders":"","keywords":"Cepstrum; Fault (geology); Bearing (navigation); Wavelet transform; Computer science; Wavelet; Speech recognition; Pattern recognition (psychology); Artificial intelligence; Geology; Seismology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005509429,0.0002444658,0.0002429488,0.0007589393,0.00004862238,0.0001514994,0.0002941251,0.0002006175,0.00002436489],"category_scores_gemma":[0.0001412519,0.0002601476,0.00004995272,0.0008417597,0.00002647694,0.0003325666,0.0001007111,0.00102597,0.00002591367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002339396,"about_ca_system_score_gemma":0.00001611878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007402062,"about_ca_topic_score_gemma":0.00002022799,"domain_scores_codex":[0.9981337,0.00003846171,0.0003075244,0.000432317,0.0004426362,0.0006454133],"domain_scores_gemma":[0.9990389,0.0004602445,0.000006047011,0.0002702985,0.00004728386,0.0001771697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002635161,0.0002234808,0.008602775,0.005496145,0.0002214463,0.0003387769,0.005329567,0.5070253,0.308302,0.002854516,0.00726422,0.1543154],"study_design_scores_gemma":[0.0001408411,0.00003048366,0.003018201,0.0004033073,0.000004292327,0.000006761387,0.00001415383,0.910624,0.0733647,0.0001608721,0.01197563,0.0002567222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9788607,0.003065964,0.01394774,0.0004070234,0.000119755,0.0009838281,0.00003641397,0.002054769,0.0005238419],"genre_scores_gemma":[0.9952767,0.001311159,0.001553051,0.000004820879,0.000106843,0.001590416,0.00002329635,0.0001098718,0.0000238103],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4035987,"threshold_uncertainty_score":0.9999851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0311687926440876,"score_gpt":0.3545799085319435,"score_spread":0.3234111158878559,"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."}}