{"id":"W2170565150","doi":"10.1177/1077546311417276","title":"Bearing system health condition monitoring using a wavelet cross-spectrum analysis technique","year":2011,"lang":"en","type":"article","venue":"Journal of Vibration and Control","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University; Carleton University","funders":"Core Research for Evolutional Science and Technology; Natural Sciences and Engineering Research Council of Canada","keywords":"Rolling-element bearing; Bearing (navigation); Wavelet; Pattern recognition (psychology); Computer science; Fault detection and isolation; Condition monitoring; Vibration; Statistic; Wavelet transform; Feature extraction; Fault (geology); Ball bearing; Signal processing; Bandwidth (computing); Artificial intelligence; Engineering; Electronic engineering; Acoustics; Mathematics; Statistics; Telecommunications; Digital signal processing; Actuator","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":[0.0004006177,0.0003904577,0.0003117928,0.001229393,0.0001477126,0.00028935,0.0002361907,0.0003665317,0.0005664085],"category_scores_gemma":[0.001003778,0.0001404802,0.0002224768,0.0006337198,0.0001869037,0.0004858767,0.0002123908,0.0002407769,0.0001661905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001191793,"about_ca_system_score_gemma":0.0001795139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005714249,"about_ca_topic_score_gemma":0.0005322311,"domain_scores_codex":[0.9997447,0.0000404659,0.00001596797,0.00005437165,0.0001293381,0.00001515884],"domain_scores_gemma":[0.9996216,0.0001175544,0.00006034007,0.00003547088,0.0001497629,0.00001521791],"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":[0.0003404556,0.00021326,0.006006347,0.0001896969,0.00008532105,0.0002722677,0.0001349588,0.02203034,0.3947419,0.001692623,0.0008072872,0.5734854],"study_design_scores_gemma":[0.0000375298,0.0006583003,0.04016869,0.00002881148,0.00009988982,0.000965537,0.00007439765,0.7819863,0.1721191,0.0009000163,0.00290051,0.00006097314],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1837645,0.0004840014,0.8133449,0.00007571582,0.00006546287,0.00006345484,0.00007102954,0.0005708741,0.001560063],"genre_scores_gemma":[0.8595486,0.0003836658,0.1388782,0.00003683168,0.00005152814,0.0000475745,0.0001382702,0.00002813228,0.0008871832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001229393,"threshold_uncertainty_score":0.002118707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01656955919129616,"score_gpt":0.2959640053398491,"score_spread":0.2793944461485529,"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."}}