{"id":"W2151941337","doi":"10.1142/s0219878908001521","title":"FAULT DIAGNOSIS OF AN INDUSTRIAL MACHINE THROUGH SENSOR FUSION","year":2008,"lang":"en","type":"article","venue":"International Journal of Information Acquisition","topic":"Industrial Technology and Control Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Artificial neural network; Robustness (evolution); Fault (geology); Accelerometer; Microphone; Fast Fourier transform; Feature vector; Feature (linguistics); Vibration; Fuzzy logic; Fault detection and isolation; Pattern recognition (psychology); Algorithm; 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":[0.0005420054,0.0005637156,0.0005821569,0.0005362337,0.0003149381,0.0005086794,0.0005693777,0.0007407608,0.0007268843],"category_scores_gemma":[0.001114536,0.0002081657,0.0004320588,0.000292067,0.0003799014,0.0008904932,0.0004706146,0.0005385628,0.0001876923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004958745,"about_ca_system_score_gemma":0.0003802797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002287036,"about_ca_topic_score_gemma":0.00162966,"domain_scores_codex":[0.9996099,0.00005827025,0.00002900542,0.00008052069,0.0001850532,0.00003731808],"domain_scores_gemma":[0.9997448,0.00008063161,0.0000398659,0.00002949881,0.00009540685,0.00000962561],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004691911,0.0001097554,0.003185078,0.0003183366,0.0001311772,0.0003463995,0.0002374346,0.5230495,0.07890443,0.006143769,0.0008058982,0.3862989],"study_design_scores_gemma":[0.00001026765,0.0000924116,0.0009884338,0.00001527866,0.00002366288,0.00007389565,0.00002039162,0.9801992,0.01494068,0.002747953,0.0008733511,0.00001449205],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04541943,0.0003692646,0.9521672,0.000110956,0.0000520359,0.00003344844,0.00002954946,0.0007246499,0.001093454],"genre_scores_gemma":[0.8662354,0.0002500735,0.1324054,0.0000550873,0.00002324682,0.00004037113,0.00006185191,0.00001218237,0.0009164672],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002287036,"threshold_uncertainty_score":0.004547417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01695096198053401,"score_gpt":0.2373228495620291,"score_spread":0.2203718875814951,"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."}}