{"id":"W4388712170","doi":"10.1016/j.ymssp.2023.110905","title":"Cumulative spectrum distribution entropy for rotating machinery fault diagnosis","year":2023,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Sample entropy; Entropy (arrow of time); Nonparametric statistics; Frequency domain; Time domain; Computer science; Pattern recognition (psychology); Maximum entropy spectral estimation; Time–frequency analysis; Algorithm; Artificial intelligence; Principle of maximum entropy; Engineering; Mathematics; Statistics; Telecommunications; Physics; Computer vision","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":[0.000515822,0.0002312992,0.0003360717,0.00007666246,0.0002350949,0.0002038182,0.0001118524,0.0001333831,0.000009503306],"category_scores_gemma":[0.0001089704,0.0002060704,0.0000743937,0.0003134131,0.00001780448,0.0001996085,0.00004941204,0.0001910947,0.000006917264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007163449,"about_ca_system_score_gemma":0.00001065243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006566217,"about_ca_topic_score_gemma":0.000007210007,"domain_scores_codex":[0.9985961,0.00004679894,0.0004173867,0.0003079712,0.0002217191,0.00041005],"domain_scores_gemma":[0.9993403,0.000319067,0.00008101761,0.00009417003,0.00004860153,0.0001168592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001569172,0.0002970576,0.01228565,0.01082524,0.0003863045,0.00009376119,0.001438183,0.1038486,0.04934351,0.0525519,0.02325549,0.7455174],"study_design_scores_gemma":[0.0002775694,0.00008974645,0.0002663364,0.0003841849,0.00002611564,0.00000624363,0.00005616773,0.9860695,0.006988893,0.003060109,0.002506145,0.0002689736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3024573,0.001185811,0.6919955,0.0002544838,0.0002548274,0.001185131,0.000218673,0.002335505,0.0001127647],"genre_scores_gemma":[0.9975345,0.00006700918,0.001073163,0.00001769215,0.0002771763,0.0008139181,0.0001369593,0.00005283161,0.00002671845],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8822209,"threshold_uncertainty_score":0.8403305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01798089631102149,"score_gpt":0.2842311013447034,"score_spread":0.2662502050336819,"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."}}