{"id":"W3000406901","doi":"10.1109/access.2020.2966529","title":"An Optimization Tendency Guiding Mode Decomposition Method for Bearing Fault Detection Under Varying Speed Conditions","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Government of Jiangsu Province; National Natural Science Foundation of China; China Postdoctoral Science Foundation; University of Ottawa","keywords":"Computer science; Fault detection and isolation; Feature extraction; Fault (geology); Noise (video); Control theory (sociology); Time–frequency analysis; Chirp; Interference (communication); Instantaneous phase; Feature (linguistics); Algorithm; Bearing (navigation); Artificial intelligence; Pattern recognition (psychology); Radar","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003812143,0.000726256,0.0004029766,0.0004718427,0.0002137944,0.0003849842,0.0003963806,0.0004100134,0.001313009],"category_scores_gemma":[0.0008243702,0.0002427433,0.0005338758,0.0004047629,0.0002270707,0.0006277254,0.0003864036,0.0006058941,0.0003202424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002267418,"about_ca_system_score_gemma":0.0005758588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002191912,"about_ca_topic_score_gemma":0.002057107,"domain_scores_codex":[0.9998479,0.00002958104,0.00001087207,0.00003929713,0.00005854294,0.00001385613],"domain_scores_gemma":[0.9998386,0.00005622498,0.00002375146,0.00001187228,0.00005815682,0.00001141968],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002220888,0.0000952028,0.002651121,0.0003048336,0.00008025677,0.0001680432,0.0002393464,0.3135354,0.08192554,0.01512172,0.003540063,0.5821164],"study_design_scores_gemma":[0.000005871753,0.0000322569,0.0003216498,0.000006102798,0.000006850373,0.00003320308,0.00001179035,0.995137,0.002655786,0.0008141741,0.0009679592,0.000007347787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00895068,0.0001596035,0.9899885,0.00005078083,0.00002047454,0.00001734924,0.00002080264,0.0001077102,0.0006841363],"genre_scores_gemma":[0.3198844,0.0006517153,0.6742734,0.00009651087,0.00005573234,0.0001812385,0.0002604858,0.0001214508,0.004475043],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002191912,"threshold_uncertainty_score":0.004392445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05434793591622073,"score_gpt":0.4236023204325016,"score_spread":0.3692543845162809,"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."}}