{"id":"W2903672252","doi":"10.36001/phme.2018.v4i1.488","title":"A study on the use of discrete event data for prognostics and health management: discovery of association rules","year":2018,"lang":"en","type":"article","venue":"PHM Society European Conference","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Université de Lorraine","keywords":"Prognostics; Association rule learning; Event (particle physics); Data mining; Event data; Computer science; Interval (graph theory); Transaction data; Association (psychology); Apriori algorithm; Database transaction; Mathematics; Analytics; Database; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01325639,0.000492806,0.000995792,0.004829153,0.0006011827,0.003322698,0.001317222,0.001232856,0.0006407122],"category_scores_gemma":[0.06774425,0.0004071805,0.001189138,0.006895958,0.001001101,0.00448986,0.0007113364,0.001697681,0.000160873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008391296,"about_ca_system_score_gemma":0.0009887208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001535459,"about_ca_topic_score_gemma":0.0007114774,"domain_scores_codex":[0.9872573,0.006476267,0.001165662,0.001577797,0.003300388,0.0002226359],"domain_scores_gemma":[0.7988583,0.1821936,0.005592891,0.006122716,0.006669886,0.0005625361],"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":[0.0009608926,0.001151735,0.1639155,0.002484122,0.001059451,0.00168323,0.00236253,0.07713729,0.004170595,0.0891128,0.002092239,0.6538696],"study_design_scores_gemma":[0.0001033254,0.001134997,0.03931282,0.001002686,0.0006948369,0.002723236,0.002484849,0.8462047,0.009849965,0.07954638,0.01679264,0.0001496091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3285621,0.01166085,0.6466474,0.003871078,0.0002136847,0.0004094247,0.0009225067,0.0002100447,0.007502982],"genre_scores_gemma":[0.773359,0.004698207,0.2198449,0.0003112238,0.0001848798,0.0001808761,0.0007519432,0.0000198336,0.0006491673],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01325639,"threshold_uncertainty_score":0.07010728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2333095167144948,"score_gpt":0.3334906185868505,"score_spread":0.1001811018723557,"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."}}