{"id":"W4407690833","doi":"10.1109/tim.2025.3542867","title":"Multiscale Dynamic Weight-Based Mixed Convolutional Neural Network for Fault Diagnosis of Rotating Machinery","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Key Research and Development Plan of Tianjin; Science and Technology Department of Henan Province; National Natural Science Foundation of China; Key Project of Research and Development Plan of Hunan Province","keywords":"Convolutional neural network; Computer science; Artificial neural network; Fault (geology); Artificial intelligence; Backpropagation; Pattern recognition (psychology)","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":[0.0002535489,0.0005318386,0.0003153082,0.0004122338,0.0001466988,0.000330733,0.0006691091,0.0004439654,0.0006896385],"category_scores_gemma":[0.0005806424,0.0002225937,0.0004604891,0.0003420535,0.0002282149,0.000757534,0.000427983,0.0003967673,0.000136157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006675065,"about_ca_system_score_gemma":0.0004378216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008100359,"about_ca_topic_score_gemma":0.008634772,"domain_scores_codex":[0.9998584,0.00001570293,0.000007946284,0.0000418879,0.00005431294,0.00002162879],"domain_scores_gemma":[0.9998975,0.00002219909,0.00001819795,0.00001391618,0.00004121435,0.000006926663],"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":[0.0001404959,0.00005311291,0.002799393,0.00009379973,0.00008590891,0.0001448446,0.0000521756,0.7490587,0.03147974,0.006228632,0.001081155,0.208782],"study_design_scores_gemma":[9.091116e-7,0.00001119323,0.0002153459,0.00000142266,0.000006280742,0.00001095549,0.000001579743,0.9977797,0.001400209,0.0003907357,0.0001794678,0.00000225237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06781438,0.0007750304,0.9285137,0.0001586329,0.00006402373,0.00002125343,0.00007957425,0.0005738771,0.001999513],"genre_scores_gemma":[0.9324268,0.0004362036,0.06448494,0.0000575609,0.00002461796,0.00003080927,0.0001250555,0.00002798125,0.002386111],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008100359,"threshold_uncertainty_score":0.01610643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01635028957290777,"score_gpt":0.271119633759266,"score_spread":0.2547693441863582,"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."}}