{"id":"W2809717913","doi":"10.3233/jifs-169533","title":"Automatic instantaneous frequency order (IFO) extraction via integration strategy and multi-demodulation for bearing fault diagnosis under variable speed operation","year":2018,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Government of Jiangsu Province; National Natural Science Foundation of China; University of Ottawa","keywords":"Fault (geology); Computer science; Bearing (navigation); Instantaneous phase; Demodulation; SIGNAL (programming language); Tachometer; Resampling; Algorithm; Control theory (sociology); Pattern recognition (psychology); Artificial intelligence; Detector; Telecommunications; Filter (signal processing); Computer vision","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003749044,0.0005624997,0.0004359694,0.001430563,0.0002656572,0.0003471125,0.0004075003,0.0004340597,0.0009387262],"category_scores_gemma":[0.001085869,0.0002095965,0.0003146221,0.0006312143,0.0002452892,0.000798626,0.0003441151,0.0003083563,0.0003680038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001907166,"about_ca_system_score_gemma":0.0003114619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007692967,"about_ca_topic_score_gemma":0.001314114,"domain_scores_codex":[0.9997455,0.00003821195,0.00001764797,0.0000548316,0.0001211135,0.0000226744],"domain_scores_gemma":[0.9996468,0.0001296127,0.00006881355,0.00004121525,0.00009888232,0.00001473388],"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":[0.0002141793,0.00009042838,0.002022972,0.0001756506,0.00002797136,0.0001619838,0.0002311205,0.01150292,0.2060796,0.001953205,0.0005559971,0.7769839],"study_design_scores_gemma":[0.00006245226,0.0004113618,0.01381121,0.00004380613,0.0001240099,0.001175821,0.0001833373,0.7467861,0.2285294,0.00341728,0.005360475,0.00009482342],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04023473,0.0002880583,0.9580739,0.00003960226,0.00002339738,0.00004308701,0.00002479047,0.0004528178,0.0008197],"genre_scores_gemma":[0.4110825,0.0002895265,0.5875717,0.00003860175,0.00002972272,0.00004438668,0.00007990615,0.00006730589,0.0007963609],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001430563,"threshold_uncertainty_score":0.00314033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02844509471809332,"score_gpt":0.3112465699819402,"score_spread":0.2828014752638469,"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."}}