{"id":"W1806901146","doi":"10.1016/j.ymssp.2015.08.019","title":"Bearing fault diagnosis under variable rotational speed via the joint application of windowed fractal dimension transform and generalized demodulation: A method free from prefiltering and resampling","year":2015,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Demodulation; Resampling; Fault (geology); Mathematics; Algorithm; SIGNAL (programming language); Envelope (radar); Frequency modulation; Instantaneous phase; Dimension (graph theory); Control theory (sociology); Time–frequency analysis; Computer science; Artificial intelligence; Filter (signal processing); Bandwidth (computing); Telecommunications; Computer vision; Radar","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":[0.0007682975,0.000182543,0.0003310729,0.00005761571,0.0001435654,0.0001328542,0.00008666884,0.0001333342,0.000002874982],"category_scores_gemma":[0.00004624437,0.0001420066,0.00002557008,0.0001239687,0.00002699389,0.0002627237,0.00007242153,0.0001723937,1.437122e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000455151,"about_ca_system_score_gemma":0.00001478366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009323748,"about_ca_topic_score_gemma":0.00001687848,"domain_scores_codex":[0.9987379,0.00007900532,0.0004595293,0.0002819631,0.0002776163,0.0001639834],"domain_scores_gemma":[0.9993122,0.0002380396,0.000114643,0.0001360093,0.00008923253,0.0001098546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001034794,0.00007288434,0.002199572,0.0008746358,0.0001521059,0.000002031098,0.001491066,0.1564284,0.6817422,0.003644274,0.00003122591,0.1532582],"study_design_scores_gemma":[0.0006035808,0.00004255484,0.0009826234,0.0002826141,0.00005348831,0.00001458464,0.0001017756,0.9554576,0.02996088,0.01225676,0.00007266822,0.0001708859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3618179,0.00107051,0.6365482,0.00006333409,0.00002510704,0.0003565567,0.000009921077,0.00008674806,0.00002171672],"genre_scores_gemma":[0.9353315,0.00002992755,0.06435679,0.00002225797,0.00009553694,0.0001172659,0.00001659238,0.00002852277,0.000001638831],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7990292,"threshold_uncertainty_score":0.5790861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02986455027624096,"score_gpt":0.2791647333803998,"score_spread":0.2493001831041589,"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."}}