{"id":"W2077898824","doi":"10.1115/1.1596552","title":"Mechanical Fault Detection Based on the Wavelet De-Noising Technique","year":2004,"lang":"en","type":"article","venue":"Journal of vibration and acoustics","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":139,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wavelet; Morlet wavelet; Thresholding; Impulse (physics); Computer science; Pattern recognition (psychology); Impulse noise; Wavelet transform; Fault detection and isolation; Artificial intelligence; Acoustics; Mathematics; Algorithm; Speech recognition; Discrete wavelet transform; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003566188,0.00008147775,0.00009259793,0.0000926366,0.00006311924,0.0000530004,0.00006685958,0.00008322595,0.000009156531],"category_scores_gemma":[0.0001992071,0.00005749483,0.00003884491,0.00008952327,0.00001359194,0.00008841427,0.0000062104,0.0003009446,5.974952e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009015641,"about_ca_system_score_gemma":0.00002513778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001508206,"about_ca_topic_score_gemma":0.000001353608,"domain_scores_codex":[0.9994789,0.00002687525,0.0002165506,0.00004375862,0.0001479822,0.00008589973],"domain_scores_gemma":[0.9996414,0.000107153,0.00007404232,0.00007418813,0.00005819591,0.00004498745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001496902,0.00004745169,0.00001074178,0.00002799874,0.00001047278,0.00001164541,0.0000462703,0.2119248,0.7769073,0.0006971133,0.0004243534,0.009876879],"study_design_scores_gemma":[0.0001982453,0.0001969319,0.0002318519,0.0001003656,0.00001965144,0.0000620595,0.00002159493,0.4120817,0.5840749,0.002738931,0.0001997321,0.00007404778],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06069408,0.00001658102,0.9382555,0.0006473052,0.00007983537,0.0001024471,0.000001633575,0.00007804794,0.0001246061],"genre_scores_gemma":[0.9647055,0.0000760343,0.03462701,0.000450987,0.000120727,0.000004068055,4.714039e-7,0.0000143948,8.658686e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9040114,"threshold_uncertainty_score":0.2344571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007402419149247546,"score_gpt":0.2425579489439803,"score_spread":0.2351555297947328,"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."}}