{"id":"W2139626997","doi":"10.1109/icphm.2011.6024364","title":"Detrended fluctuation analysis of vibration signals for bearing fault detection","year":2011,"lang":"en","type":"article","venue":"","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Bearing (navigation); Detrended fluctuation analysis; Vibration; Computer science; Fault (geology); Wavelet; Fault detection and isolation; Wavelet transform; Rolling-element bearing; Pattern recognition (psychology); Artificial intelligence; Engineering; Mathematics; Acoustics","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":[0.0004942719,0.000525425,0.0004109016,0.001077528,0.0001750543,0.0003921819,0.000265267,0.0003485127,0.001026213],"category_scores_gemma":[0.002467482,0.000122479,0.0003541199,0.001052882,0.0001866841,0.0005276315,0.0002266149,0.0004384357,0.0003215975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001399256,"about_ca_system_score_gemma":0.0001521422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005562914,"about_ca_topic_score_gemma":0.0006094312,"domain_scores_codex":[0.9997287,0.00009047381,0.00002197224,0.00004451576,0.0001004091,0.00001387065],"domain_scores_gemma":[0.9991428,0.000520778,0.00009177527,0.00008276108,0.0001360599,0.00002585268],"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":[0.0004087071,0.0001882637,0.007242013,0.0003992717,0.0001438339,0.0006826553,0.0001291667,0.07468502,0.132414,0.006705814,0.002760393,0.7742408],"study_design_scores_gemma":[0.00001339453,0.0001861406,0.01917963,0.00002996209,0.00005111716,0.0003673807,0.00004442791,0.9492484,0.02081404,0.004645694,0.005366107,0.0000536492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0813017,0.002694567,0.9126191,0.0002360787,0.000194622,0.000059097,0.00032598,0.0005888036,0.001980154],"genre_scores_gemma":[0.7977362,0.001972921,0.1981002,0.00006606297,0.0001970007,0.00006187042,0.00052265,0.00006783677,0.001275128],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001077528,"threshold_uncertainty_score":0.003433049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06886848767368153,"score_gpt":0.2270310370826503,"score_spread":0.1581625494089687,"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."}}