{"id":"W7117482822","doi":"10.1016/j.aei.2025.104284","title":"Iteratively modified variational mode extraction (IMVME): A noise-robust transient feature nonlinear extraction approach for aero-engine fault diagnosis","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Wuhan University of Science and Technology; Natural Science Foundation of Hubei Province; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Transient (computer programming); Feature extraction; Control theory (sociology); Robustness (evolution); Feature (linguistics); Noise (video); Filter (signal processing); Adaptive filter; Fault (geology); Pattern recognition (psychology)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001991883,0.0005038392,0.0004370947,0.0004788121,0.0001312742,0.0001197043,0.00025405,0.00031522,0.000006528985],"category_scores_gemma":[0.0001529173,0.0005488545,0.0001809368,0.0005516087,0.00001751179,0.001278622,0.00002563061,0.0006057495,0.00000311057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003757816,"about_ca_system_score_gemma":0.00003377653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004204802,"about_ca_topic_score_gemma":0.000002049059,"domain_scores_codex":[0.9982118,0.00001132691,0.0007606177,0.0002305021,0.0003017541,0.0004840028],"domain_scores_gemma":[0.9989026,0.0003126877,0.0001193172,0.0003596455,0.0001934868,0.000112222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002896835,0.0001035487,0.00001844862,0.0009701449,0.0001173807,7.589938e-7,0.0004649899,0.9819131,0.00452001,0.001306601,0.001548613,0.00900747],"study_design_scores_gemma":[0.0009115766,0.00004784799,0.000302571,0.0001793051,0.00007240607,0.00001095201,0.00005886828,0.9624847,0.0158772,0.00004843322,0.01951792,0.0004882882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008436241,0.0001889649,0.9857156,0.00008795082,0.0005753452,0.001303119,0.0002479564,0.001658341,0.001786454],"genre_scores_gemma":[0.2159212,0.0003151871,0.7805376,0.00008346946,0.0001288496,0.002136556,0.0007102389,0.00008267654,0.00008419466],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.207485,"threshold_uncertainty_score":0.9996963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009170146203006907,"score_gpt":0.2729387942657313,"score_spread":0.2637686480627244,"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."}}