{"id":"W4407371963","doi":"10.1109/ticps.2025.3539997","title":"Open-Set Fault Diagnosis for Industrial Rotating Machines Based on Trustworthy Deep Learning","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Cyber-Physical Systems","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Trustworthiness; Set (abstract data type); Fault (geology); Artificial intelligence; Computer science; Deep learning; Machine learning; Data science; Computer security; Seismology; Geology; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001384064,0.0006202485,0.000717962,0.0007950133,0.0003629143,0.0007750527,0.001169691,0.0009849323,0.0005581417],"category_scores_gemma":[0.00500316,0.0002370655,0.0003551446,0.0003321955,0.0009298902,0.001420929,0.00152441,0.001453494,0.0001363831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007959396,"about_ca_system_score_gemma":0.0007614231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001811629,"about_ca_topic_score_gemma":0.002415009,"domain_scores_codex":[0.9993272,0.0001260007,0.00004275257,0.000156885,0.0002503509,0.00009679087],"domain_scores_gemma":[0.997454,0.001301172,0.0003795902,0.0002873022,0.0004677579,0.0001102458],"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":[0.000615461,0.0001960746,0.007723875,0.0001335583,0.00006952995,0.000362234,0.0002909185,0.640236,0.01627288,0.01360322,0.002030346,0.3184659],"study_design_scores_gemma":[0.00000269255,0.00002041182,0.0002116067,0.000004920622,0.000002932594,0.00001755065,0.000005744665,0.9950807,0.001664195,0.002894015,0.00009150201,0.00000372189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1147728,0.0003129931,0.8828627,0.0002956406,0.0000350928,0.0000358304,0.00004924913,0.0006167669,0.001018938],"genre_scores_gemma":[0.9637191,0.00006665619,0.03555034,0.00008321875,0.0000192556,0.00001811655,0.00008006344,0.00001963962,0.0004435832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001811629,"threshold_uncertainty_score":0.007319748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03230842958292795,"score_gpt":0.2738772048123987,"score_spread":0.2415687752294707,"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."}}