{"id":"W4378218810","doi":"10.1177/1748006x231174960","title":"Optimal condition based maintenance using attribute Bayesian control chart","year":2023,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Control chart; Control limits; Condition-based maintenance; Partially observable Markov decision process; Bayesian probability; Computer science; Statistical process control; Markov chain; Reliability engineering; Data mining; Chart; Statistics; Machine learning; Process (computing); Engineering; Artificial intelligence; Markov model; 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":[],"consensus_categories":[],"category_scores_codex":[0.001639813,0.0001526953,0.0003995751,0.0001247199,0.00008775347,0.00001381559,0.0001915736,0.0001485612,0.000006515905],"category_scores_gemma":[0.001360237,0.000108326,0.0002099181,0.0004352237,0.0002094669,0.0002971741,0.00002822569,0.0003464432,4.587988e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009935709,"about_ca_system_score_gemma":0.00005536111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005156332,"about_ca_topic_score_gemma":2.670905e-7,"domain_scores_codex":[0.9985844,0.00002027008,0.000733052,0.0001357792,0.0003188392,0.0002076808],"domain_scores_gemma":[0.9986863,0.0001192107,0.000393629,0.0001035873,0.0005982762,0.00009895906],"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":[0.0001107405,0.00004069926,0.0008394595,0.0002838152,0.00003289399,4.901361e-7,0.00006575807,0.9892824,0.007313479,0.001533454,0.0003146439,0.0001821614],"study_design_scores_gemma":[0.001339042,0.0001349934,0.001526401,0.0004775263,0.0001398455,0.00001900589,0.0001716125,0.965938,0.02843781,0.001174735,0.0005002539,0.0001408097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7659639,0.00007096962,0.2326703,0.0002580167,0.0006725856,0.0002291363,0.00005882264,0.00005094736,0.00002535487],"genre_scores_gemma":[0.9942309,0.0005012501,0.005168711,0.0000103565,0.00006820879,0.000003422102,0.000001887949,0.00001197507,0.000003282996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.228267,"threshold_uncertainty_score":0.4417405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007363713392144142,"score_gpt":0.2101663102479544,"score_spread":0.2028025968558103,"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."}}