{"id":"W3193718491","doi":"10.22215/etd/2021-14504","title":"Remaining Useful Life Prediction of a Turbofan Engine Using Deep Layer Recurrent Neural Networks","year":2021,"lang":"en","type":"dissertation","venue":"","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Turbofan; Prognostics; Artificial neural network; Perceptron; Computer science; Nonlinear autoregressive exogenous model; Recurrent neural network; Artificial intelligence; Multilayer perceptron; Deep learning; Cascade; Machine learning; Engineering; Data mining; Automotive engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001848869,0.0004459599,0.0006060013,0.0002984975,0.0000456596,0.00005145825,0.0001968658,0.0005009335,0.0002426122],"category_scores_gemma":[0.0001582198,0.0004784828,0.0002106502,0.0003953877,0.00001030301,0.000150361,0.00003175277,0.0007343003,9.344595e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000123443,"about_ca_system_score_gemma":0.00003724084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007303462,"about_ca_topic_score_gemma":0.0002446475,"domain_scores_codex":[0.998179,0.00005552084,0.000743659,0.0003621818,0.0003180527,0.0003415554],"domain_scores_gemma":[0.9990339,0.00009562446,0.0001750657,0.0003975903,0.000170171,0.000127642],"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":[0.00006017474,0.0001780817,0.006558539,0.002382907,0.0006347367,0.00003594108,0.001724273,0.932199,0.005856538,0.0001342022,0.005520381,0.04471522],"study_design_scores_gemma":[0.0001660324,0.00004436851,0.004243603,0.0006798141,0.0001506066,0.000006043124,0.0001931061,0.9869257,0.006952573,0.00001203153,0.0002757839,0.0003503069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9218938,0.006538391,0.05708602,0.000016734,0.004443464,0.0008594629,0.00005678063,0.002322221,0.006783117],"genre_scores_gemma":[0.989917,0.0007402959,0.006375255,0.00002523995,0.0004613305,0.00008019823,0.002122665,0.0001784702,0.00009947937],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06802326,"threshold_uncertainty_score":0.9997667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02028695277497469,"score_gpt":0.2813741652462175,"score_spread":0.2610872124712428,"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."}}