{"id":"W2512345112","doi":"10.1109/icphm.2016.7542867","title":"A hydrogenerator model-based failure detection framework to support asset management","year":2016,"lang":"en","type":"article","venue":"","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; École de Technologie Supérieure","funders":"Hydro-Québec","keywords":"Asset management; Asset (computer security); Risk analysis (engineering); Engineering; Process (computing); Hydroelectricity; Warning system; Hydropower; Decision support system; Reliability engineering; Computer science; Business; Artificial intelligence; Computer security; Finance","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001336815,0.0008670011,0.0006970523,0.001446635,0.0004051236,0.001268434,0.001656179,0.0009354464,0.002275226],"category_scores_gemma":[0.002963198,0.0003217649,0.0009149676,0.0006666512,0.0004977223,0.001277181,0.000863737,0.00103953,0.0004553142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001199532,"about_ca_system_score_gemma":0.001611487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01225658,"about_ca_topic_score_gemma":0.01269946,"domain_scores_codex":[0.9994729,0.0001772976,0.00003483069,0.00008951751,0.0001798166,0.00004568506],"domain_scores_gemma":[0.9991133,0.0004414791,0.00009813791,0.00007166344,0.0002333908,0.00004194643],"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":[0.00002888909,0.00007357696,0.0006640608,0.00006367568,0.00005869542,0.00009077012,0.00006613573,0.9176315,0.001739364,0.04847946,0.001183684,0.02992026],"study_design_scores_gemma":[0.000003282206,0.00001242281,0.00007404785,0.000005836174,0.000006363306,0.00001075471,0.000006577694,0.9901699,0.0002137082,0.008632133,0.0008608814,0.000003955002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003001817,0.00009691985,0.995087,0.0001153594,0.00001386112,0.00004439185,0.0001222564,0.0005202624,0.0009980558],"genre_scores_gemma":[0.3655536,0.0003669552,0.6301648,0.0001057856,0.00005029883,0.0003024561,0.000740331,0.0001012388,0.002614508],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01225658,"threshold_uncertainty_score":0.02437043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006665123968799598,"score_gpt":0.2540992657737643,"score_spread":0.2474341418049648,"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."}}