{"id":"W2791102065","doi":"10.1016/j.cie.2018.03.026","title":"Optimal Bayesian control policy for gear shaft fault detection using hidden semi-Markov model","year":2018,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Fundamental Research Funds for the Central Universities; Graduate Research and Innovation Projects of Jiangsu Province; China Scholarship Council; Central University Basic Research Fund of China; National Natural Science Foundation of China","keywords":"Hidden Markov model; Unobservable; Hidden semi-Markov model; Bayesian probability; Fault detection and isolation; Markov process; Engineering; Computer science; Mathematical optimization; Markov chain; Markov model; Control theory (sociology); Variable-order Markov model; Mathematics; Artificial intelligence; Control (management); Econometrics; Statistics; Machine learning","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":[0.001911436,0.0008802405,0.002208909,0.0007829331,0.0005946471,0.001171645,0.001501428,0.001523205,0.002851998],"category_scores_gemma":[0.007178925,0.0008672245,0.00069153,0.0005174069,0.001272911,0.001437205,0.001168057,0.001744496,0.0004362294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001674375,"about_ca_system_score_gemma":0.002683969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01755006,"about_ca_topic_score_gemma":0.0119498,"domain_scores_codex":[0.999006,0.0002263457,0.00005443276,0.0002523586,0.0002385431,0.0002223271],"domain_scores_gemma":[0.9953074,0.003501215,0.0003777805,0.0001287434,0.0005385883,0.0001462998],"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":[0.0002837224,0.00008169522,0.0004657689,0.00007401907,0.00003881139,0.00004847705,0.0000514552,0.9682084,0.001231964,0.007587811,0.0005956116,0.0213322],"study_design_scores_gemma":[0.00001172782,0.00001383873,0.0001068385,0.00000490852,0.000005949808,0.000004118903,0.0000022775,0.997685,0.0001833431,0.00193753,0.00003924361,0.00000515158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05092087,0.0003701574,0.9455642,0.0004906472,0.00007151145,0.00005087978,0.00008530304,0.0003652338,0.00208122],"genre_scores_gemma":[0.9727419,0.0001805104,0.02440685,0.0001127669,0.00004115424,0.00007328537,0.0001038271,0.00003590871,0.002303743],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01755006,"threshold_uncertainty_score":0.03489578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01614199093494456,"score_gpt":0.2273488761541833,"score_spread":0.2112068852192387,"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."}}