{"id":"W2990913701","doi":"10.1115/1.4045491","title":"NBLSTM: Noisy and Hybrid Convolutional Neural Network and BLSTM-Based Deep Architecture for Remaining Useful Life Estimation","year":2019,"lang":"en","type":"article","venue":"Journal of Computing and Information Science in Engineering","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Convolutional neural network; Computer science; Robustness (evolution); Deep learning; Artificial intelligence; Software deployment; Process (computing); Artificial neural network; Machine learning; Pattern recognition (psychology)","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":[0.001058106,0.0001051289,0.0001736843,0.0004097275,0.00006731736,0.0001449869,0.00009244402,0.0000306284,7.908981e-7],"category_scores_gemma":[0.0003546808,0.0000996754,0.00002034072,0.000234762,0.00004075754,0.001402558,0.00002968334,0.0002041403,1.645137e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004274414,"about_ca_system_score_gemma":0.00003334435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001094942,"about_ca_topic_score_gemma":2.805133e-7,"domain_scores_codex":[0.9991038,0.000006961498,0.0004281446,0.00006635651,0.0002003036,0.0001943888],"domain_scores_gemma":[0.9993433,0.0002842719,0.0001334466,0.00005360456,0.00009383923,0.00009147967],"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":[0.00000547884,0.000001564379,0.005844506,0.0001260416,0.00000250835,2.488645e-7,0.0002834291,0.9845579,0.000180575,0.0001738588,0.00002058307,0.008803361],"study_design_scores_gemma":[0.0004012516,0.00005852642,0.02970253,0.0002206393,0.000003659588,0.00006499924,0.00002579432,0.9689274,0.0001889945,0.00005819167,0.0002446714,0.0001032957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6225301,0.000103842,0.3769825,0.00005790202,0.0001666914,0.00009483425,9.049265e-7,0.00003950804,0.0000236843],"genre_scores_gemma":[0.9351121,0.0000181997,0.06471557,0.00008542438,0.00005902633,0.000001724956,0.000001846603,0.000005957255,1.154065e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.312582,"threshold_uncertainty_score":0.4064645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004802300276549434,"score_gpt":0.2380221536025608,"score_spread":0.2332198533260113,"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."}}