{"id":"W2263289545","doi":"10.1016/j.neunet.2016.01.003","title":"An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines","year":2016,"lang":"en","type":"article","venue":"Neural Networks","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":147,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Qatar National Research Fund","keywords":"Computer science; Artificial neural network; Fault detection and isolation; Ensemble forecasting; Support vector machine; Multilayer perceptron; Radial basis function; Perceptron; Fault (geology); Ensemble learning; Artificial intelligence; Machine learning; Data mining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001557035,0.0006008201,0.001076055,0.0007004691,0.0004389403,0.0007586736,0.0009699885,0.0008142277,0.0007410113],"category_scores_gemma":[0.003750229,0.000317748,0.0003424015,0.0005344246,0.0002556279,0.001133636,0.0008859556,0.0009196969,0.0002551066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000534912,"about_ca_system_score_gemma":0.000701285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002754207,"about_ca_topic_score_gemma":0.003999898,"domain_scores_codex":[0.9994414,0.000123023,0.00005195259,0.0001492607,0.0001619238,0.00007245353],"domain_scores_gemma":[0.9984984,0.000433996,0.0001545219,0.000171761,0.00067912,0.00006226296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005612478,0.0002465298,0.004488557,0.0001595262,0.0002588693,0.0001116272,0.00009370076,0.4309445,0.01326151,0.004341868,0.002770458,0.5427616],"study_design_scores_gemma":[0.000006049208,0.00005772924,0.0006236785,0.000006356848,0.00002804446,0.00002065823,0.000006371547,0.9961249,0.002071845,0.0006123731,0.000434695,0.000007332404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1092324,0.001581427,0.8855604,0.0001994364,0.0003434725,0.00004973762,0.0001155306,0.0008103246,0.002107245],"genre_scores_gemma":[0.9127288,0.0004073212,0.0838683,0.0001142652,0.0001294651,0.00006568486,0.0002779499,0.00004084876,0.002367288],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002754207,"threshold_uncertainty_score":0.008234501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005378963002423152,"score_gpt":0.2123901332610705,"score_spread":0.2070111702586473,"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."}}