{"id":"W1991269832","doi":"10.1115/1.4027215","title":"Multiple-Model Sensor and Components Fault Diagnosis in Gas Turbine Engines Using Autoassociative Neural Networks","year":2014,"lang":"en","type":"article","venue":"Journal of Engineering for Gas Turbines and Power","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Fault detection and isolation; Artificial neural network; Jet engine; Fault (geology); Computer science; Scheme (mathematics); Component (thermodynamics); Isolation (microbiology); Real-time computing; Artificial intelligence; Control engineering; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002747219,0.0002357402,0.0004559379,0.0002056354,0.00004173899,0.00005949495,0.00006812428,0.0001211673,0.000001531318],"category_scores_gemma":[0.0001660575,0.0002062333,0.0001077578,0.0001129514,0.00001366121,0.0001799596,0.00001566598,0.0002558353,1.186988e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005783241,"about_ca_system_score_gemma":0.000003793662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001495901,"about_ca_topic_score_gemma":0.000009143497,"domain_scores_codex":[0.9989878,0.00001932066,0.000461325,0.0001201967,0.000123126,0.0002882414],"domain_scores_gemma":[0.9993116,0.000286429,0.0001139766,0.00007373767,0.00008689315,0.0001274119],"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":[0.00003845191,0.00001447296,0.004099214,0.00007288153,0.00006793986,0.000003978721,0.0002013976,0.9902459,0.004396161,0.00001956364,0.00006949329,0.000770501],"study_design_scores_gemma":[0.001643673,0.00009655668,0.004006844,0.0001477078,0.00003367125,0.00007420794,0.00003405734,0.9922892,0.0001455672,0.00001438375,0.001300627,0.0002135475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9612742,0.001389943,0.0359949,0.00009614252,0.001004024,0.000167488,0.000009632904,0.00005384007,0.000009814515],"genre_scores_gemma":[0.9977703,0.0001504085,0.001681548,0.00002927789,0.0002973122,0.00001142009,0.000001775773,0.00004560704,0.00001234236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03649609,"threshold_uncertainty_score":0.840995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01082256075952532,"score_gpt":0.2202250014245031,"score_spread":0.2094024406649778,"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."}}