{"id":"W2508151180","doi":"10.1109/icphm.2016.7542877","title":"Integrated Hilbert Huang technique for bearing defects detection","year":2016,"lang":"en","type":"article","venue":"","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Bearing (navigation); Reliability (semiconductor); Reliability engineering; Fault (geology); Aerospace; Computer science; Condition monitoring; Automotive industry; Fault detection and isolation; Main bearing; Production (economics); Power (physics); Engineering; Automotive engineering; Artificial intelligence; Mechanical engineering; Electrical engineering; Aerospace engineering","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":[0.0006669556,0.0004667341,0.0004470426,0.001201883,0.0001761709,0.0004125446,0.0004532216,0.0005347013,0.001847062],"category_scores_gemma":[0.001373733,0.0001570888,0.0003599819,0.0009395714,0.000334285,0.0009725994,0.0003785042,0.000615089,0.0006442505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000227268,"about_ca_system_score_gemma":0.0003061903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005984243,"about_ca_topic_score_gemma":0.0007515674,"domain_scores_codex":[0.9995554,0.0001072664,0.00001737586,0.00006177563,0.0002276133,0.00003059014],"domain_scores_gemma":[0.9995048,0.0002577981,0.0000465634,0.00005977109,0.0001049825,0.00002607376],"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":[0.000302569,0.0001227972,0.00112827,0.0002059315,0.00007505566,0.0001651074,0.0001827998,0.02607099,0.2123678,0.009157436,0.001531767,0.7486895],"study_design_scores_gemma":[0.00003269175,0.0003946633,0.004889915,0.0000156922,0.00006042568,0.0007316618,0.00007297617,0.8868349,0.09261391,0.006583891,0.007706068,0.00006322371],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02157097,0.0005613499,0.9760491,0.00006350083,0.00003839543,0.0000276472,0.00004192084,0.0005004082,0.001146711],"genre_scores_gemma":[0.4342183,0.0008273126,0.5603415,0.00006147359,0.0001144347,0.00008360487,0.0001933008,0.00009066036,0.004069421],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001847062,"threshold_uncertainty_score":0.006179035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008897180560610592,"score_gpt":0.2516563460777073,"score_spread":0.2427591655170967,"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."}}