{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007338867,0.0002956638,0.000492612,0.0001247373,0.0002387163,0.00006799624,0.0001344884,0.0002085721,0.00006073649],"category_scores_gemma":[0.0002596861,0.0002924633,0.0002981846,0.0008418515,0.0001573265,0.00030422,0.000001862951,0.0004001823,0.00000385375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002803232,"about_ca_system_score_gemma":0.00006676897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002010095,"about_ca_topic_score_gemma":0.00001502698,"domain_scores_codex":[0.9978885,0.00009034779,0.0006690996,0.0007428673,0.0002655612,0.0003436034],"domain_scores_gemma":[0.9983612,0.0004479797,0.00006915016,0.0005565581,0.0002300031,0.0003351333],"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":[0.0001128983,0.0001240473,0.00163127,0.0003958779,0.0001498553,2.515107e-7,0.0003003956,0.9960417,0.000134628,0.00006681464,0.00007224311,0.000970035],"study_design_scores_gemma":[0.000596492,0.0002972652,0.0202193,0.00002009762,0.0005645309,6.107727e-7,0.0001193197,0.9761984,0.0004681144,0.0008146808,0.0004350291,0.0002661938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1784707,0.00001489643,0.8177199,0.00103763,0.0003384962,0.001136562,0.0002387857,0.0006103655,0.0004326853],"genre_scores_gemma":[0.9846221,0.0001014432,0.01455846,0.0001673247,0.00005860498,0.0004076443,0.00002889621,0.00003199941,0.0000235209],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8061514,"threshold_uncertainty_score":0.9999527,"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."}}