{"id":"W3110752823","doi":"10.1109/tr.2020.3032157","title":"Probabilistic Analysis for Remaining Useful Life Prediction and Reliability Assessment","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Department of National Defence; National Research Council Canada; Okanagan University College; Government of Canada; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Reliability (semiconductor); Probabilistic logic; Computer science; Reliability engineering; Bayesian probability; Workload; Inference; Posterior probability; Data mining; Predictive inference; Bayesian inference; Machine learning; Grid; Set (abstract data type); Artificial intelligence; Engineering; Frequentist inference; 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":[0.002505026,0.0009539438,0.001083253,0.001613211,0.0003998682,0.0009845907,0.001495807,0.0009002369,0.001545404],"category_scores_gemma":[0.007014193,0.0005426813,0.001089751,0.00117619,0.0007925078,0.001574737,0.0008680368,0.001138011,0.0002655484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000825208,"about_ca_system_score_gemma":0.0009825601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006135117,"about_ca_topic_score_gemma":0.003156774,"domain_scores_codex":[0.9988664,0.0003733225,0.00005601786,0.0002315397,0.0003805262,0.00009216645],"domain_scores_gemma":[0.9975666,0.001687825,0.0002569037,0.0001594398,0.0002913558,0.00003798415],"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":[0.00002229655,0.00001534733,0.0008058912,0.00005225375,0.00003334746,0.00005009495,0.0000259015,0.9574482,0.0008152939,0.01796881,0.00037894,0.02238356],"study_design_scores_gemma":[9.951274e-7,0.000005499409,0.0001193085,0.000002735384,0.000004766669,0.000009966589,0.000002134718,0.9937232,0.0001212772,0.005865969,0.0001404501,0.000003720157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004058755,0.0002333363,0.9948613,0.00006988829,0.000008807782,0.00001076259,0.00003997697,0.0001161241,0.0006010105],"genre_scores_gemma":[0.8673981,0.001228699,0.1288256,0.00009758455,0.0001544563,0.0001581673,0.0003836845,0.00008368935,0.001670046],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006135117,"threshold_uncertainty_score":0.01324797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01834430008340569,"score_gpt":0.2353295278360714,"score_spread":0.2169852277526657,"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."}}