{"id":"W4214545748","doi":"10.3390/act11030067","title":"Remaining Useful Life Prediction of an Aircraft Turbofan Engine Using Deep Layer Recurrent Neural Networks","year":2022,"lang":"en","type":"article","venue":"Actuators","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Turbofan; Prognostics; Artificial neural network; Perceptron; Mean squared error; Multilayer perceptron; Nonlinear autoregressive exogenous model; Computer science; Recurrent neural network; Cascade; Range (aeronautics); Artificial intelligence; Engineering; Machine learning; Data mining; Automotive engineering; Statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003135267,0.0002120579,0.0002518004,0.0002054661,0.0001215329,0.00002005566,0.0002441578,0.00007582866,0.0001486764],"category_scores_gemma":[0.00007805554,0.00023888,0.00008427362,0.0003642568,0.00002006405,0.0002406616,0.0001155586,0.0004748671,5.447096e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001659851,"about_ca_system_score_gemma":0.00001589242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004664527,"about_ca_topic_score_gemma":0.00001034553,"domain_scores_codex":[0.9986953,0.00009135338,0.0003829134,0.0002326534,0.0002911609,0.0003066004],"domain_scores_gemma":[0.9993097,0.00007933009,0.00008498987,0.0003556649,0.0000296113,0.000140715],"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":[0.00001571832,0.00008252959,0.01856483,0.00004066426,0.00006186701,0.000009279362,0.0007599923,0.9476984,0.0007142302,0.00002881049,0.0008212668,0.03120243],"study_design_scores_gemma":[0.0001875289,0.0001516943,0.0054995,0.00001953149,0.00003770966,0.00001329733,0.0001007674,0.9912236,0.001467739,0.00002874238,0.001078985,0.0001909454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9641915,0.0002393392,0.03354284,0.00002407036,0.0007689137,0.0002415498,0.00002841008,0.0008736161,0.00008975788],"genre_scores_gemma":[0.9962087,0.00003055882,0.003277777,0.00006768851,0.0002016015,0.00006466181,0.00006884242,0.00007886264,0.000001273584],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04352518,"threshold_uncertainty_score":0.9741242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01870601865604508,"score_gpt":0.2586147832932766,"score_spread":0.2399087646372315,"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."}}