{"id":"W2319957760","doi":"10.1016/j.ymssp.2016.03.007","title":"Pseudo-fault signal assisted EMD for fault detection and isolation in rotating machines","year":2016,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Syncrude","keywords":"Hilbert–Huang transform; Fault (geology); Envelope (radar); SIGNAL (programming language); Fault detection and isolation; Engineering; Signal processing; Noise (video); Instantaneous phase; Computer science; Electronic engineering; White noise; Artificial intelligence; Digital signal processing; Electrical engineering; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005546281,0.0002160896,0.0003085194,0.0001298585,0.0001401997,0.0001511975,0.00006893952,0.0001826942,0.000006004525],"category_scores_gemma":[0.00007927199,0.0001586288,0.00003617103,0.0001459926,0.00001855998,0.0003397461,0.0000278809,0.0001407166,7.190281e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006274383,"about_ca_system_score_gemma":0.000009767449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008138542,"about_ca_topic_score_gemma":0.00008691351,"domain_scores_codex":[0.9987163,0.00006374504,0.0004702602,0.0003144748,0.0001615965,0.000273606],"domain_scores_gemma":[0.9994271,0.0002503342,0.00009738035,0.00006988052,0.00006754557,0.00008772623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003253266,0.00001989092,0.0009382147,0.0006221438,0.00001145272,0.000002263477,0.00008434267,0.0003679236,0.4185336,0.0001636359,0.00001534348,0.5792087],"study_design_scores_gemma":[0.0006666076,0.000131929,0.0008375141,0.0008502391,0.00001708059,0.0000321623,0.00003694051,0.9818969,0.01414585,0.0009732284,0.0001467908,0.000264737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3967695,0.0005663227,0.6017469,0.00004736338,0.00005979594,0.000460162,0.000008767875,0.0002901035,0.00005106212],"genre_scores_gemma":[0.9954346,0.00002250448,0.004053023,0.0000146413,0.000147958,0.0002647337,0.000002801474,0.0000424728,0.00001725276],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.981529,"threshold_uncertainty_score":0.6468697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01407843238471032,"score_gpt":0.2655997657567273,"score_spread":0.2515213333720169,"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."}}