{"id":"W2079322063","doi":"10.1115/1.1687391","title":"Pattern Recognition for Automatic Machinery Fault Diagnosis","year":2004,"lang":"en","type":"article","venue":"Journal of vibration and acoustics","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Calgary","funders":"National Research Council Canada","keywords":"Bearing (navigation); Signature (topology); Pattern recognition (psychology); Rolling-element bearing; Fault (geology); Computer science; Feature (linguistics); Artificial intelligence; Feature extraction; Vibration; Simple (philosophy); Data mining; Engineering; Mathematics; Acoustics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001399431,0.00009366443,0.000145938,0.0001157223,0.00003813182,0.00005632508,0.0000520705,0.0000624176,0.00002055509],"category_scores_gemma":[0.0001508326,0.00008286507,0.00005249662,0.00005602563,0.00001104972,0.0002261794,0.000006539402,0.0001182004,0.000001712973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003363174,"about_ca_system_score_gemma":0.00001379108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002107562,"about_ca_topic_score_gemma":0.000004418963,"domain_scores_codex":[0.9994121,0.000009328407,0.0003306094,0.00004880907,0.0001109035,0.00008830067],"domain_scores_gemma":[0.9995718,0.0001142803,0.0001120247,0.00004987728,0.0000948814,0.00005711907],"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":[0.00001645549,0.0003071443,0.004241533,0.0009089949,0.000146916,0.00002732113,0.0006822973,0.08278174,0.02137576,0.00007342912,0.01941202,0.8700264],"study_design_scores_gemma":[0.003197258,0.0009455627,0.02274671,0.0009941936,0.0003594598,0.0002333894,0.0001682253,0.8847325,0.0685892,0.01616209,0.001230899,0.0006404619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3571892,0.0001282436,0.6419049,0.000314878,0.0001902396,0.000122756,0.00002450288,0.00008626597,0.00003901307],"genre_scores_gemma":[0.9531089,0.0007177427,0.04561899,0.0002672134,0.0002283291,0.00002398351,0.00001250384,0.00002093039,0.000001410397],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.869386,"threshold_uncertainty_score":0.337914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0135446178601209,"score_gpt":0.2664975933092091,"score_spread":0.2529529754490882,"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."}}