{"id":"W2024040073","doi":"10.1007/s00170-014-6651-4","title":"Optimal condition-based maintenance policy for a partially observable system with two sampling intervals","year":2014,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Partially observable Markov decision process; Sampling (signal processing); Unobservable; Control limits; Mathematics; Bayesian probability; Posterior probability; Observable; Statistics; Markov process; Mathematical optimization; Limit (mathematics); Control theory (sociology); Computer science; Markov decision process; Process (computing); Control chart; Control (management); Econometrics; Filter (signal processing); Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003975199,0.0001688255,0.0002617788,0.0002955512,0.0000770167,0.00005410837,0.0007343602,0.0000735213,0.000005476215],"category_scores_gemma":[0.0002481304,0.0001167606,0.00009713067,0.0001024195,0.0001147678,0.0002218624,0.00004196079,0.0002565603,0.000002588516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003462767,"about_ca_system_score_gemma":0.00006450616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006989589,"about_ca_topic_score_gemma":0.00001658241,"domain_scores_codex":[0.9988906,0.00001822488,0.0004576769,0.0001402291,0.0002245484,0.0002687342],"domain_scores_gemma":[0.9987929,0.0002213874,0.0003019103,0.0002343127,0.0004074056,0.00004211583],"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":[0.00023281,0.000014911,0.00001958678,0.00004801729,0.00009479818,0.000004251955,0.00002737976,0.974439,0.00370919,0.01536637,0.00005948913,0.00598425],"study_design_scores_gemma":[0.005066353,0.0006703651,0.0002043368,0.001280507,0.00006940783,0.0004490035,0.000488797,0.4696985,0.4827528,0.01323943,0.02565398,0.0004265172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2864859,0.00003020355,0.7099453,0.002596903,0.0004483329,0.0001811349,0.000009754169,0.0001717809,0.0001307422],"genre_scores_gemma":[0.9032089,0.00002803704,0.0962805,0.0001480938,0.0002244771,0.00004452042,0.000006107571,0.00003146377,0.00002785872],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6167231,"threshold_uncertainty_score":0.476136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008677916299172497,"score_gpt":0.2501384070482084,"score_spread":0.2414604907490359,"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."}}