{"id":"W1968486765","doi":"10.1115/imece2004-59349","title":"Gearbox Fault Detection Using Empirical Mode Decomposition","year":2004,"lang":"en","type":"article","venue":"","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hilbert–Huang transform; Hilbert transform; Wavelet transform; SIGNAL (programming language); Fault (geology); Instantaneous phase; Envelope (radar); Computer science; Fault detection and isolation; Time–frequency analysis; Wavelet; Vibration; Signal processing; Pattern recognition (psychology); Mode (computer interface); Algorithm; Artificial intelligence; Speech recognition; Spectral density; Acoustics; Computer vision; White noise; Telecommunications","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":[0.00004415971,0.0001137951,0.00009672804,0.0001001385,0.00004583838,0.00002934372,0.00006549568,0.00009082894,0.00002891074],"category_scores_gemma":[0.000009384128,0.0001145876,0.00004513521,0.0001387061,0.00001004443,0.0001176485,0.00001593038,0.0001312064,0.00002675908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002391094,"about_ca_system_score_gemma":0.000006780005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000283746,"about_ca_topic_score_gemma":0.0002018908,"domain_scores_codex":[0.9994535,0.000009463576,0.0001445926,0.0001227169,0.0001071577,0.0001626116],"domain_scores_gemma":[0.9997665,0.00001385549,0.00001135801,0.0001357004,0.00002026407,0.00005234652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000006278823,0.00008134842,0.001120265,0.00003860811,0.00002641687,0.000009413653,0.0001675062,0.6816381,0.3020633,0.000264606,0.000177411,0.01440683],"study_design_scores_gemma":[0.0001671139,0.00002567878,0.0008140021,0.00001784321,0.000009060944,0.00002222703,0.000005901116,0.4817019,0.5151163,0.001650058,0.0003140602,0.0001558085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6325762,0.00001707649,0.3638007,0.00003968333,0.00007055165,0.00008860577,0.000001213095,0.001205151,0.002200857],"genre_scores_gemma":[0.9636436,0.00001252941,0.03611578,0.0001018414,0.00007065962,0.00001851245,0.0000039652,0.0000290266,0.000004121983],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3310674,"threshold_uncertainty_score":0.4672745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01837593251710458,"score_gpt":0.3889476228447492,"score_spread":0.3705716903276446,"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."}}