{"id":"W4322707015","doi":"10.1109/tim.2023.3250308","title":"Reliable and Intelligent Fault Diagnosis With Evidential VGG Neural Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"China Scholarship Council; Royal Society","keywords":"Artificial neural network; Computer science; Artificial intelligence; Fault (geology); Noise (video); Machine learning; Reliability (semiconductor); Process (computing); Data mining; Image (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009009037,0.0008313523,0.0005622435,0.0008791948,0.0002759855,0.000826236,0.001234019,0.001228578,0.0005140055],"category_scores_gemma":[0.003306018,0.0002988549,0.0004862612,0.0006063103,0.0007086138,0.001153131,0.001128157,0.001188502,0.0001607943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000822393,"about_ca_system_score_gemma":0.0005649216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003269411,"about_ca_topic_score_gemma":0.003469395,"domain_scores_codex":[0.9996235,0.00009226507,0.00002746231,0.00008909983,0.0001239895,0.00004371756],"domain_scores_gemma":[0.9991729,0.0004126962,0.0001356614,0.00007883029,0.0001739805,0.00002584091],"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":[0.0001690747,0.00006259882,0.002493393,0.0001087073,0.00006558961,0.000168638,0.0001598627,0.6914128,0.005253193,0.01030632,0.001266853,0.2885329],"study_design_scores_gemma":[0.000003508709,0.0000189693,0.0002105671,0.000008172984,0.000006841521,0.00002157535,0.000008705509,0.9921692,0.001064332,0.00626174,0.0002215747,0.000004737325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02920576,0.0004949375,0.9683356,0.0003475662,0.00004664668,0.00002949479,0.00006108467,0.0004046275,0.001074296],"genre_scores_gemma":[0.889302,0.0002426272,0.1089272,0.0002056821,0.00004600131,0.00004545229,0.0001453029,0.00002940002,0.001056296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003269411,"threshold_uncertainty_score":0.006500721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02777630287197556,"score_gpt":0.2647597088848442,"score_spread":0.2369834060128687,"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."}}