{"id":"W2893702958","doi":"10.36001/phmconf.2018.v10i1.500","title":"New Adaptive Prognostics Approach Based on Hybrid Feature Selection with Application to Point Machine Monitoring","year":2018,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu","keywords":"Prognostics; Feature (linguistics); Feature selection; Component (thermodynamics); Field (mathematics); Computer science; Point (geometry); Selection (genetic algorithm); Model selection; Degradation (telecommunications); Artificial intelligence; Pattern recognition (psychology); Engineering; Data mining; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005570782,0.0005911833,0.0006506239,0.0009119058,0.0002573134,0.0003740612,0.0006067025,0.0004239512,0.0008127278],"category_scores_gemma":[0.001035873,0.0002094044,0.0005153163,0.0006285443,0.0002630539,0.0006875025,0.0004243382,0.0004557694,0.0002145808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002114014,"about_ca_system_score_gemma":0.0002204468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001264162,"about_ca_topic_score_gemma":0.0009925932,"domain_scores_codex":[0.9996186,0.00005970417,0.00002668496,0.00009177704,0.0001721611,0.00003093149],"domain_scores_gemma":[0.9995586,0.000169089,0.00005759941,0.00005102918,0.0001476116,0.00001605913],"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":[0.0002351397,0.0001285536,0.005336759,0.0001127511,0.0001092554,0.0002740985,0.0001259342,0.2301554,0.04049235,0.002481034,0.001600759,0.7189478],"study_design_scores_gemma":[0.00001152293,0.000121708,0.002395722,0.000005361986,0.00002513377,0.000135045,0.00001164925,0.9893588,0.00543592,0.0013029,0.001180509,0.00001571337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02008198,0.0002375317,0.9785791,0.00005367624,0.00002320539,0.00002713345,0.00003001784,0.0005530564,0.0004143621],"genre_scores_gemma":[0.7508988,0.0002782943,0.2469198,0.0000661591,0.00009446481,0.00009552074,0.0001405622,0.0000588283,0.001447724],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001264162,"threshold_uncertainty_score":0.002946138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01045020301869191,"score_gpt":0.2460267562699881,"score_spread":0.2355765532512962,"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."}}