{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006914934,0.0005997759,0.0006645591,0.001081801,0.0002925012,0.0008854771,0.0009447873,0.0009161893,0.004362811],"category_scores_gemma":[0.001663567,0.0001991224,0.0004921239,0.001350273,0.0004730787,0.001159947,0.0004822864,0.0007797055,0.002812178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003490389,"about_ca_system_score_gemma":0.0005267516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007686566,"about_ca_topic_score_gemma":0.0005967953,"domain_scores_codex":[0.9993881,0.0001468313,0.00006786759,0.0001405521,0.0002095585,0.00004708293],"domain_scores_gemma":[0.9995771,0.0001119176,0.00005551082,0.00009916695,0.0001433916,0.00001291386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007813319,0.0000484137,0.0005396082,0.0003914174,0.00004365454,0.0001547762,0.00004231595,0.0144974,0.03474791,0.02229171,0.007230985,0.9199337],"study_design_scores_gemma":[0.00009207077,0.0003910894,0.004028828,0.0002014099,0.0001062263,0.001387526,0.00008722433,0.7058282,0.06820926,0.08141445,0.1381686,0.00008498248],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003084534,0.001366836,0.9918469,0.0002386175,0.0001180465,0.00006363304,0.0001321219,0.00143929,0.001710073],"genre_scores_gemma":[0.08773623,0.002393914,0.9029999,0.0002559891,0.0002006027,0.0002898586,0.0008496426,0.00008518811,0.005188682],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004362811,"threshold_uncertainty_score":0.01459509,"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."}}