{"id":"W2036901142","doi":"10.1108/13552511311315977","title":"Vibration‐ and acoustic‐emissions based novelty detection of fretted bearings","year":2013,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"","keywords":"Novelty detection; Fault detection and isolation; Novelty; Engineering; Feature vector; Vibration; Feature (linguistics); Fault (geology); Acoustic emission; Pattern recognition (psychology); Principal component analysis; Dimensionality reduction; Condition monitoring; Reduction (mathematics); Identification (biology); Computer science; Artificial intelligence; Acoustics; Mathematics","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":[0.0007019917,0.0005862776,0.0006017903,0.001031353,0.0001586521,0.0004231038,0.0006664082,0.0004414984,0.0006814309],"category_scores_gemma":[0.003186215,0.0002130029,0.0004652479,0.0004493131,0.0003638968,0.0006788963,0.0004706716,0.0004453631,0.0002273809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002541077,"about_ca_system_score_gemma":0.0001852167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003928349,"about_ca_topic_score_gemma":0.0006798196,"domain_scores_codex":[0.9991431,0.00009830055,0.00004171089,0.000138183,0.0005241164,0.00005459429],"domain_scores_gemma":[0.9976661,0.0008513704,0.0004385124,0.0002087703,0.0007683539,0.00006699411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007687114,0.0002519847,0.01938442,0.0003021844,0.00009942865,0.0004057093,0.0002740835,0.01475726,0.621349,0.0005169142,0.0004940777,0.3413962],"study_design_scores_gemma":[0.0000614141,0.002605444,0.1139044,0.00004344434,0.0001354973,0.002127988,0.0002811239,0.422508,0.4541987,0.001289327,0.002726682,0.0001179448],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6506591,0.0004179441,0.3473097,0.0001416814,0.0001197092,0.00008790227,0.0001011008,0.0005027757,0.0006601875],"genre_scores_gemma":[0.9297482,0.0001346349,0.06923622,0.00003261998,0.00004498893,0.00003555667,0.0001326791,0.00002348593,0.0006116379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001031353,"threshold_uncertainty_score":0.003712535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0120034111469476,"score_gpt":0.2468153676994277,"score_spread":0.2348119565524801,"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."}}