{"id":"W4388292562","doi":"10.1177/14759217231199427","title":"Intelligent fault diagnosis of rotating machinery under variable working conditions based on deep transfer learning with fusion of local and global time–frequency features","year":2023,"lang":"en","type":"article","venue":"Structural Health Monitoring","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Feature extraction; Time–frequency analysis; Fault (geology); Frequency domain; Pattern recognition (psychology); Wavelet; Time domain; Engineering; Machine learning; Computer vision; Radar; Telecommunications","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":[0.000226476,0.0002569547,0.0003806857,0.0001880428,0.0001953358,0.00002274033,0.000122151,0.0001034425,0.00002452391],"category_scores_gemma":[0.00003413956,0.0002267422,0.00004983879,0.0006383603,0.00006824327,0.00009388741,0.00002818234,0.0004059697,9.915333e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001992399,"about_ca_system_score_gemma":0.00004348531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007121562,"about_ca_topic_score_gemma":0.00005193914,"domain_scores_codex":[0.9984902,0.00009084788,0.0004377873,0.0002563338,0.0003247549,0.0004001253],"domain_scores_gemma":[0.9992058,0.0003641585,0.000082301,0.0001649238,0.00005499265,0.0001278514],"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":[0.00002950215,0.00001163961,0.1883278,0.0006729967,0.00003754208,0.00000432276,0.0002960311,0.7563803,0.001126214,0.0005254965,0.00002136994,0.05256686],"study_design_scores_gemma":[0.000878816,0.0008786015,0.4704661,0.004225368,0.00006069054,0.00001719167,0.0006480834,0.4819086,0.03849666,0.001749504,0.00002327387,0.0006470736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9856859,0.0004694711,0.01229345,0.00008143837,0.0002329902,0.0003850373,0.00004129679,0.0005360683,0.0002742749],"genre_scores_gemma":[0.9908633,0.0001637582,0.008710762,0.00001844139,0.00007563818,0.00007233791,0.00004934869,0.00004356304,0.000002907471],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2821383,"threshold_uncertainty_score":0.9246279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01219483233381012,"score_gpt":0.3030530786587498,"score_spread":0.2908582463249397,"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."}}