{"id":"W3021550080","doi":"10.3182/20060829-4-cn-2909.00225","title":"FAULT DETECTION OF ROTATING MACHINERY FROM BICOHERENCE ANALYSIS OF VIBRATION DATA","year":2006,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Bicoherence; SIGNAL (programming language); Vibration; Signature (topology); Fault (geology); Signal processing; Fault detection and isolation; Representation (politics); Computer science; Linearity; Engineering; Control theory (sociology); Pattern recognition (psychology); Artificial intelligence; Bispectrum; Electronic engineering; Acoustics; Mathematics; Digital signal processing; Spectral density; Physics; Telecommunications","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":[0.000183277,0.0001625931,0.0003565308,0.0003863272,0.00003256964,0.00003836134,0.0003671928,0.0000974982,0.00003700409],"category_scores_gemma":[0.0001406188,0.0001713364,0.00007503341,0.0009635644,0.00003495185,0.0005507208,0.00009202506,0.0001257197,0.000001682738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002722311,"about_ca_system_score_gemma":0.00000710929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00343171,"about_ca_topic_score_gemma":0.0005432537,"domain_scores_codex":[0.9988138,0.000006569403,0.0005010163,0.0002776146,0.0002522939,0.0001486858],"domain_scores_gemma":[0.9992979,0.00006918132,0.0002193554,0.0002551248,0.0001326586,0.00002583753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008515609,0.00005989981,0.2317817,0.0001584976,0.0003540214,3.331251e-7,0.0002047034,0.006363707,0.7261219,0.00006057486,0.0009015654,0.0339846],"study_design_scores_gemma":[0.00007027432,0.00001970007,0.1001743,0.0000385549,0.0002560482,2.376287e-7,0.00002883959,0.6171426,0.2818377,0.0002280395,0.00008170707,0.0001219479],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9720193,0.0002493774,0.02590591,0.00001094356,0.00004674075,0.000165084,0.0002237545,0.00041843,0.0009605072],"genre_scores_gemma":[0.9809225,0.00002175064,0.01868505,0.000005074485,0.0000643941,0.00002833005,0.0002344772,0.00002571323,0.00001268073],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6107789,"threshold_uncertainty_score":0.6986896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01144150273291579,"score_gpt":0.2531926869178164,"score_spread":0.2417511841849006,"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."}}