{"id":"W3037839743","doi":"10.1155/2020/5804509","title":"KPCA and AE Based Local-Global Feature Extraction Method for Vibration Signals of Rotating Machinery","year":2020,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Pattern recognition (psychology); Feature extraction; Artificial intelligence; Autoencoder; Discriminative model; Kernel principal component analysis; Classifier (UML); Computer science; Vibration; Feature (linguistics); Support vector machine; Engineering; Deep learning; Kernel method; Acoustics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004616096,0.0009644516,0.0009150517,0.001003033,0.0002972752,0.0005449241,0.0005807824,0.0007084808,0.001595393],"category_scores_gemma":[0.001329859,0.000347196,0.001084662,0.0009542029,0.0003679947,0.00106828,0.0005113668,0.001044406,0.0006935034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003129797,"about_ca_system_score_gemma":0.0005913839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002446918,"about_ca_topic_score_gemma":0.002415004,"domain_scores_codex":[0.9995289,0.00005756563,0.00004855942,0.0001466414,0.0001769286,0.00004136313],"domain_scores_gemma":[0.9995646,0.0001249728,0.00006759541,0.00005916758,0.0001652718,0.000018439],"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":[0.0001909702,0.0001069329,0.001565433,0.0002629739,0.0001413132,0.0002312589,0.000091677,0.1597559,0.06009334,0.004121311,0.002949516,0.7704894],"study_design_scores_gemma":[0.000009102208,0.00007214307,0.002102943,0.00001212314,0.00002987635,0.0002231739,0.00002163238,0.9784632,0.01540013,0.00141397,0.002223006,0.00002870387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009089154,0.0004079261,0.9892307,0.00008993553,0.00004010548,0.00002586825,0.00006099943,0.0004804081,0.0005749422],"genre_scores_gemma":[0.4957497,0.001361145,0.4944684,0.0001411547,0.0001075901,0.0001656219,0.0006764055,0.0001382258,0.007191759],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002446918,"threshold_uncertainty_score":0.005337179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01339506670612453,"score_gpt":0.2896009061937411,"score_spread":0.2762058394876166,"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."}}