{"id":"W3212263828","doi":"10.1016/j.ress.2021.108191","title":"A deep learning predictive model for selective maintenance optimization","year":2021,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Predictive maintenance; Component (thermodynamics); Benchmarking; Modular design; Reliability engineering; Turbofan; Computer science; Set (abstract data type); Maintenance actions; Engineering; Machine learning","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":[0.0004929592,0.0006056473,0.0008429515,0.0004486647,0.0002705444,0.0006434093,0.00158167,0.001184976,0.002899228],"category_scores_gemma":[0.001571284,0.0005512155,0.0005487545,0.0006190499,0.0003677294,0.0009273809,0.0007273357,0.001749431,0.0006761292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008123039,"about_ca_system_score_gemma":0.001123568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01605339,"about_ca_topic_score_gemma":0.01973684,"domain_scores_codex":[0.9998286,0.00002562414,0.000008208039,0.00005765223,0.00004661648,0.00003323995],"domain_scores_gemma":[0.9995009,0.0002576234,0.00004269529,0.00004512567,0.0001295457,0.00002419013],"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":[0.00005505064,0.0000582852,0.0004206104,0.00002857826,0.00003015186,0.00002752387,0.00000918007,0.9418269,0.0008207437,0.002547251,0.002248606,0.05192716],"study_design_scores_gemma":[0.00000150009,0.000003212977,0.00003135212,0.000001268809,0.000002314424,0.000001587769,3.860957e-7,0.9992231,0.00008948156,0.0005708769,0.00007406428,8.973674e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06429047,0.001543347,0.9242557,0.0008616763,0.0002066736,0.00004257655,0.0009370067,0.001949849,0.005912764],"genre_scores_gemma":[0.9174051,0.0004758428,0.06969254,0.0003630251,0.0001242483,0.0001156938,0.001258634,0.0001274022,0.0104376],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01605339,"threshold_uncertainty_score":0.0319199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00378129399622761,"score_gpt":0.177417110569024,"score_spread":0.1736358165727964,"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."}}