{"id":"W1984276989","doi":"10.1088/0957-0233/24/8/085004","title":"An enhanced Hilbert–Huang transform technique for bearing condition monitoring","year":2013,"lang":"en","type":"article","venue":"Measurement Science and Technology","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Rolling-element bearing; Hilbert transform; Robustness (evolution); Bearing (navigation); Noise reduction; Entropy (arrow of time); SIGNAL (programming language); Signal processing; Feature extraction; Pattern recognition (psychology); Filter (signal processing); Vibration; Artificial intelligence; Digital signal processing; Acoustics; Computer vision","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":[],"consensus_categories":[],"category_scores_codex":[0.0009670116,0.0001548613,0.0001574622,0.0005442793,0.0002217808,0.00008024661,0.0003513916,0.0001368902,0.00001302409],"category_scores_gemma":[0.0001460462,0.0001509944,0.00001902138,0.0006042442,0.0002356585,0.000653939,0.00002409216,0.0001561855,0.000004705076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002745718,"about_ca_system_score_gemma":0.00003696252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002840825,"about_ca_topic_score_gemma":0.00001117814,"domain_scores_codex":[0.9986345,0.000005192284,0.0001954151,0.0003085889,0.0004550431,0.0004012177],"domain_scores_gemma":[0.9991061,0.00001278411,0.00002727166,0.000272049,0.0005053789,0.0000764241],"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":[6.730166e-7,0.00001864013,0.0004961371,0.00003244972,0.000004398541,1.73382e-7,0.00003559112,0.00001397889,0.9351398,0.0007108641,0.00006978647,0.06347754],"study_design_scores_gemma":[0.0001438858,0.0001406814,0.0009562325,0.0000625915,0.000007339838,0.000003825523,0.00008301518,0.002346795,0.9896896,0.006064779,0.0003201785,0.000181056],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5723913,0.0003007538,0.4162528,0.0009730265,0.0002814116,0.003323577,0.000004473501,0.003241252,0.003231402],"genre_scores_gemma":[0.9855045,0.00005182538,0.0118418,0.00001260761,0.000027446,0.002538429,0.000001097924,0.00002026378,0.000002043216],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4131132,"threshold_uncertainty_score":0.6157372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0164174242761509,"score_gpt":0.2813075623755277,"score_spread":0.2648901380993768,"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."}}