{"id":"W3003471925","doi":"10.1016/j.isatra.2020.01.033","title":"Adaptive filtering enhanced windowed correlated kurtosis for multiple faults diagnosis of locomotive bearings","year":2020,"lang":"en","type":"article","venue":"ISA Transactions","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"Fundamental Research Funds for the Central Universities; Aerostatic Science Foundation; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Kurtosis; Fault (geology); SIGNAL (programming language); Noise (video); Computer science; Wavelet; Bearing (navigation); Basis (linear algebra); Engineering; Algorithm; Control theory (sociology); Electronic engineering; Artificial intelligence; Mathematics","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":[0.00005175813,0.0002507325,0.0003661907,0.0001088135,0.00008620696,0.00001313778,0.0001965766,0.0001442966,0.0001687605],"category_scores_gemma":[0.00008093043,0.0002869685,0.0002237126,0.0003480971,0.00004922016,0.0002173695,0.000008462282,0.0002676603,0.00001263865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006819965,"about_ca_system_score_gemma":0.00001284787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001093876,"about_ca_topic_score_gemma":0.00007818404,"domain_scores_codex":[0.9988852,0.00002162384,0.0003880434,0.000284306,0.0001403493,0.000280519],"domain_scores_gemma":[0.9990455,0.0004732223,0.00006521173,0.0001823046,0.00009878348,0.0001350048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000537306,0.0007759673,0.00493633,0.001036473,0.001523206,0.000009000238,0.02026642,0.4565589,0.4271665,0.00008256159,0.002961597,0.08414581],"study_design_scores_gemma":[0.0009751541,0.0002537358,0.001735432,0.0001331585,0.0001158776,9.547548e-7,0.000194204,0.1686141,0.8266088,0.00003076403,0.001031809,0.0003059295],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1713527,0.0000755423,0.8255759,0.0001663359,0.0001547291,0.000964552,0.0004243162,0.0009426904,0.0003432792],"genre_scores_gemma":[0.9849381,0.0001515707,0.01358486,0.00006414454,0.00003898039,0.001109604,0.00002679175,0.0000736212,0.00001233511],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8135854,"threshold_uncertainty_score":0.9999582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02073371352053467,"score_gpt":0.2392666355213714,"score_spread":0.2185329220008367,"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."}}