{"id":"W4285395612","doi":"10.22215/jphm.v2i1.3321","title":"Jet Engine Optimal Preventive Maintenance Scheduling Using Golden Section Search and Genetic Algorithm","year":2022,"lang":"en","type":"article","venue":"Journal of Prognostics and Health Management","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Chongqing Municipal Education Commission; Research Manitoba","keywords":"Preventive maintenance; Corrective maintenance; Jet engine; Predictive maintenance; Reliability engineering; Schedule; Aircraft maintenance; Optimal maintenance; Scheduling (production processes); Planned maintenance; Reliability (semiconductor); Engineering; Job shop scheduling; Computer science; Automotive engineering; Operations management; Mechanical engineering; Aeronautics; Power (physics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006868984,0.0007210994,0.001055053,0.001177575,0.000299421,0.000606793,0.0009772237,0.0008176795,0.001048873],"category_scores_gemma":[0.001627824,0.0004837635,0.0008754377,0.0007952729,0.0004396325,0.0004961442,0.0004246117,0.0004945582,0.0001166186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00120566,"about_ca_system_score_gemma":0.001741432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01267333,"about_ca_topic_score_gemma":0.006287068,"domain_scores_codex":[0.9997035,0.00008289862,0.00001198463,0.00005831434,0.00008407121,0.00005919059],"domain_scores_gemma":[0.9994979,0.0002870539,0.00008122543,0.00002524406,0.00007870904,0.0000297395],"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":[0.00001988787,0.00001610452,0.0002501475,0.00001512189,0.00001200332,0.00001794319,0.00001212023,0.9885448,0.0005406536,0.001558695,0.0001291733,0.00888332],"study_design_scores_gemma":[0.000005216028,0.00001253367,0.00006519971,0.000001746649,0.000004936684,0.000003379229,0.000002044955,0.9992157,0.0001466476,0.0004806629,0.00006011928,0.00000170146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09608523,0.0005682874,0.8984818,0.0001280219,0.00003812917,0.00007620007,0.00005739959,0.0003999422,0.004164915],"genre_scores_gemma":[0.8251894,0.0003210509,0.1719915,0.00005838296,0.00002362597,0.0001298218,0.000142701,0.00006262675,0.002080942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01267333,"threshold_uncertainty_score":0.02519912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0192931074806404,"score_gpt":0.2665848716997915,"score_spread":0.2472917642191511,"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."}}