{"id":"W4412494400","doi":"10.1016/j.ymssp.2025.113121","title":"Learnable wavelet-driven physically interpretable networks for bearing fault diagnosis under variable speed","year":2025,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"National Natural Science Foundation of China","keywords":"Bearing (navigation); Wavelet; Fault (geology); Variable (mathematics); Artificial intelligence; Computer science; Pattern recognition (psychology); Wave speed; Control theory (sociology); Structural engineering; Engineering; Acoustics; Mathematics; Geology; Physics; Mathematical analysis; Seismology","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":[0.0004326523,0.000521876,0.0004675464,0.0002538697,0.0001560035,0.0004134396,0.0006821881,0.0008680237,0.001210599],"category_scores_gemma":[0.002467873,0.0002658219,0.000340948,0.0002203843,0.0003927115,0.000827324,0.0006446405,0.001109167,0.0002013951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005033941,"about_ca_system_score_gemma":0.0003945918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002624832,"about_ca_topic_score_gemma":0.003293036,"domain_scores_codex":[0.9998556,0.0000286726,0.000007103488,0.00004687395,0.00003682141,0.00002476199],"domain_scores_gemma":[0.9993737,0.0003973367,0.00007364288,0.00004749888,0.0000902165,0.00001766167],"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":[0.0001529847,0.00005462964,0.000523296,0.00005966313,0.00002649048,0.0000903695,0.00004639887,0.8947014,0.005824248,0.005302143,0.0007297113,0.09248871],"study_design_scores_gemma":[0.000001803903,0.000009356379,0.00008044344,0.000001423053,0.00000184047,0.000004755117,0.000001548442,0.9982691,0.0003670351,0.001219609,0.00004205521,0.0000010982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07506184,0.000303266,0.9225597,0.0002604049,0.00006168001,0.00002325661,0.0001276306,0.0004866926,0.001115538],"genre_scores_gemma":[0.9541071,0.0001491027,0.04347134,0.00006574331,0.00003216266,0.00004098271,0.0002102981,0.00003659704,0.001886666],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002624832,"threshold_uncertainty_score":0.005219042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01049021400762463,"score_gpt":0.2581006406608103,"score_spread":0.2476104266531857,"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."}}