{"id":"W1965020189","doi":"10.4028/www.scientific.net/kem.569-570.449","title":"Gear Fault Diagnosis Using Synchro-Squeezing Transform Based Feature Analysis","year":2013,"lang":"en","type":"article","venue":"Key engineering materials","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Synchro; Hilbert–Huang transform; Wavelet transform; Signal processing; Robustness (evolution); Continuous wavelet transform; Instantaneous phase; S transform; Time–frequency analysis; SIGNAL (programming language); Engineering; Electronic engineering; Noise (video); Wavelet; Fault (geology); Computer science; Pattern recognition (psychology); Algorithm; Artificial intelligence; Discrete wavelet transform; Computer vision; Digital signal processing; White noise; Electrical engineering; Telecommunications","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":[0.0002374027,0.0004044876,0.0003763267,0.001314864,0.0001619601,0.00046529,0.0002398737,0.0004149798,0.0009702085],"category_scores_gemma":[0.001290721,0.0001290168,0.0002986393,0.0005806745,0.0002006748,0.000708989,0.0003814159,0.0003225841,0.0003239318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001423524,"about_ca_system_score_gemma":0.0002117725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005204778,"about_ca_topic_score_gemma":0.0004836257,"domain_scores_codex":[0.9998844,0.00001656125,0.000009207723,0.00002784039,0.00005121306,0.00001080955],"domain_scores_gemma":[0.99971,0.0001437623,0.00005262753,0.00002931961,0.00005126809,0.00001294949],"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":[0.000379634,0.00009398089,0.006315226,0.0001748075,0.00005108703,0.0006772431,0.0002121173,0.0483952,0.2346021,0.003999705,0.001047281,0.7040517],"study_design_scores_gemma":[0.00002332162,0.0001831664,0.00897888,0.00001752749,0.00004002506,0.0006780866,0.00008820143,0.934426,0.05057584,0.003229403,0.001730251,0.00002931476],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1203888,0.0002236838,0.8771565,0.0001153735,0.00003362937,0.0000409098,0.00008535429,0.00100655,0.0009492841],"genre_scores_gemma":[0.7824391,0.0002066782,0.2162806,0.00002281022,0.00002719704,0.00002933855,0.0001615714,0.00003945043,0.0007931917],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001314864,"threshold_uncertainty_score":0.003245711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006687560950166406,"score_gpt":0.2270522646166725,"score_spread":0.2203647036665061,"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."}}