{"id":"W2054766319","doi":"10.1109/iecon.2012.6389277","title":"A smart monitor for measurement and fault detection","year":2012,"lang":"en","type":"article","venue":"","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Bearing (navigation); Fault detection and isolation; Wavelet; Fault (geology); Feature extraction; Energy (signal processing); Vibration; SIGNAL (programming language); Computer science; Condition monitoring; Rotor (electric); Pattern recognition (psychology); Feature (linguistics); Wavelet transform; Artificial intelligence; Engineering; Electronic engineering; Acoustics; Actuator; Mathematics; Physics; Electrical 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001828246,0.00006922739,0.00006071872,0.00003586571,0.00002422015,0.0000123159,0.00002544624,0.00003791868,0.00001167871],"category_scores_gemma":[0.00003425934,0.00006230922,0.000019266,0.00002837796,0.000004396867,0.00009992917,0.000007785156,0.00003658499,0.000005035154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004846828,"about_ca_system_score_gemma":9.001425e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002595391,"about_ca_topic_score_gemma":0.00003839383,"domain_scores_codex":[0.999644,0.000004617886,0.00007434761,0.00005238799,0.00008171334,0.0001429087],"domain_scores_gemma":[0.9998243,0.00001874121,0.000006202907,0.0000719473,0.00002647008,0.0000522666],"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":[0.00001932932,0.0001469236,0.02929416,0.0004365668,0.0001256871,2.404963e-7,0.0004149412,0.00009827594,0.3383288,0.0007728675,0.03223706,0.5981252],"study_design_scores_gemma":[0.0002183065,0.0000501274,0.01621714,0.00001469859,0.00001948121,0.000003016295,0.00001888604,0.01480892,0.9056849,0.0001377953,0.06264344,0.0001832596],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5304688,0.001256852,0.4562598,0.00009441806,0.0006830344,0.0009968287,0.000006197522,0.002337947,0.007896176],"genre_scores_gemma":[0.9923698,0.00002677734,0.007156691,0.00002263556,0.0001194148,0.0002659748,6.337536e-7,0.00001701998,0.00002106132],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5979419,"threshold_uncertainty_score":0.2540896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02050345097749749,"score_gpt":0.2639110438225753,"score_spread":0.2434075928450778,"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."}}