{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002454433,0.001082642,0.001256523,0.0009833495,0.0005079894,0.00193201,0.001128876,0.0008535181,0.001739336],"category_scores_gemma":[0.006044241,0.0004045374,0.0005633869,0.0007007879,0.0008646828,0.001267651,0.0007686965,0.00105933,0.0001711482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00159279,"about_ca_system_score_gemma":0.00194934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01076833,"about_ca_topic_score_gemma":0.004091401,"domain_scores_codex":[0.9985301,0.0003408226,0.00007173085,0.0003483016,0.0005213877,0.0001875357],"domain_scores_gemma":[0.9970846,0.00144916,0.000529914,0.0001466563,0.0006632073,0.0001264973],"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":[0.0001083421,0.00004340825,0.0006143201,0.00004575992,0.00001682656,0.00003392115,0.00004003206,0.9638445,0.001472534,0.006990584,0.0004375567,0.02635228],"study_design_scores_gemma":[0.000009846106,0.00003016511,0.0001630631,0.00000435668,0.000005503632,0.00000546151,0.000002832022,0.9976237,0.0003539915,0.00165495,0.0001405876,0.000005484758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0300201,0.0003356161,0.9659247,0.0001428296,0.00003343977,0.0001277974,0.0001316164,0.0005502764,0.002733601],"genre_scores_gemma":[0.9387833,0.0002181396,0.05959844,0.00003856232,0.00002358871,0.000145114,0.0001682462,0.00003674701,0.0009878282],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01076833,"threshold_uncertainty_score":0.0214113,"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."}}