{"id":"W4402298359","doi":"10.1007/s11760-024-03545-y","title":"Enhanced fault feature extraction and bearing fault diagnosis using shearlet transform and deep learning","year":2024,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Optech (Canada)","funders":"","keywords":"Shearlet; Bearing (navigation); Fault (geology); Artificial intelligence; Feature extraction; Extraction (chemistry); Pattern recognition (psychology); Feature (linguistics); Computer science; Deep learning; Geology; Chromatography; Chemistry; Seismology; Image (mathematics); Philosophy","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":[0.0002697546,0.0002900013,0.0002561073,0.0002012027,0.0002844443,0.0008385583,0.000053983,0.0001686209,0.0000210731],"category_scores_gemma":[0.00004605144,0.0002819589,0.00004007046,0.0002284256,0.00007772528,0.001446844,0.00003585947,0.0006503332,0.000001195555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005127984,"about_ca_system_score_gemma":0.00001445671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004956047,"about_ca_topic_score_gemma":0.00001964858,"domain_scores_codex":[0.9988571,0.00003070902,0.0002224178,0.0004028377,0.0001631436,0.0003238364],"domain_scores_gemma":[0.9995835,0.0001598692,0.00003056664,0.00006556186,0.00004473808,0.0001157673],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008258716,0.00001016602,0.000423526,0.001372206,0.00002757253,0.00003277994,0.001582479,0.0003752884,0.2844559,0.000006913617,0.00005660651,0.7116483],"study_design_scores_gemma":[0.0002657315,0.00007959465,0.0008218758,0.001794892,0.0001388418,0.0001666919,0.0004505661,0.6411082,0.3515292,0.0004027431,0.002675749,0.0005658906],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6416466,0.03817799,0.3173164,0.0002300306,0.00007657843,0.0003186974,0.000005876149,0.001184462,0.001043323],"genre_scores_gemma":[0.9835941,0.00337308,0.01266958,0.00004102981,0.0001433096,0.00006255112,0.00000765508,0.00007516447,0.00003357771],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7110824,"threshold_uncertainty_score":0.9999633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008723781131386658,"score_gpt":0.2905163793592865,"score_spread":0.2817925982278998,"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."}}