{"id":"W2135761512","doi":"10.1051/matecconf/20152007001","title":"Monitoring gears by vibration measurements: Lempel-Ziv complexity and Approximate Entropy as diagnostic tools","year":2015,"lang":"en","type":"article","venue":"MATEC Web of Conferences","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Approximate entropy; Kurtosis; Vibration; Sample entropy; Computer science; Entropy (arrow of time); Pattern recognition (psychology); Signal processing; Fault detection and isolation; SIGNAL (programming language); Condition monitoring; Artificial intelligence; Speech recognition; Engineering; Statistics; 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":[0.0008720351,0.0005325005,0.000495781,0.002030089,0.0001737668,0.0009872777,0.0003745629,0.0005221601,0.0004846464],"category_scores_gemma":[0.005188509,0.0001649942,0.0002798607,0.0008576252,0.0006559035,0.001682538,0.0006543064,0.0004243465,0.0001364893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003793639,"about_ca_system_score_gemma":0.0001765373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003028411,"about_ca_topic_score_gemma":0.0002985946,"domain_scores_codex":[0.9993513,0.0002482755,0.00003694741,0.00007491636,0.0002541159,0.00003444738],"domain_scores_gemma":[0.997923,0.001459154,0.0002749284,0.0001225843,0.0001494193,0.0000710121],"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":[0.0009330227,0.0001766266,0.02145037,0.0004796193,0.0001596823,0.0004496134,0.0004559535,0.2906555,0.08915759,0.04413594,0.001492835,0.5504533],"study_design_scores_gemma":[0.00001156808,0.0001377135,0.009169343,0.00002261537,0.00002137447,0.0001845706,0.00004531876,0.9676813,0.01004965,0.01201539,0.0006078889,0.00005324447],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1898782,0.002066903,0.8045844,0.0002834266,0.0000558494,0.00006101628,0.0001685322,0.0003502546,0.002551483],"genre_scores_gemma":[0.9249911,0.0005368931,0.07360815,0.00004419258,0.00008731917,0.00004985166,0.0001093228,0.00002149716,0.0005516429],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002030089,"threshold_uncertainty_score":0.00461179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07598720333258466,"score_gpt":0.2983278691618411,"score_spread":0.2223406658292564,"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."}}