{"id":"W3138535992","doi":"10.3390/jmmp5010026","title":"Condition Monitoring of Manufacturing Processes under Low Sampling Rate","year":2021,"lang":"en","type":"article","venue":"Journal of Manufacturing and Materials Processing","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Kernel density estimation; Sampling (signal processing); Computer science; Process (computing); Condition monitoring; Industrial engineering; Estimation; Manufacturing execution system; Machine tool; Real-time computing; Reliability engineering; Manufacturing engineering; Engineering; Computer-integrated manufacturing; Systems engineering; Statistics; Mechanical engineering; Mathematics; Computer vision","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":[0.0003917206,0.0001918647,0.0004461059,0.000146034,0.0001140858,0.0002330003,0.00008688142,0.00009684835,0.00002268371],"category_scores_gemma":[0.00004689697,0.0001730385,0.00005186273,0.00006043625,0.00002597173,0.0004661476,0.00002416159,0.0001592093,0.000001360793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005053195,"about_ca_system_score_gemma":0.00006524902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004049554,"about_ca_topic_score_gemma":0.000001283719,"domain_scores_codex":[0.9986992,0.00005041979,0.000693686,0.0001365937,0.0002011199,0.0002189119],"domain_scores_gemma":[0.9992341,0.00006985358,0.0003715457,0.00008841739,0.0001516589,0.00008437521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006913101,0.00002629,0.00002218163,0.005266362,0.0001165674,0.00003841972,0.0004538349,0.0165026,0.9522235,0.000002319636,0.000008648626,0.02527017],"study_design_scores_gemma":[0.0005663708,0.00002688827,0.001918332,0.001492583,0.00004869594,0.0002963619,0.0008266177,0.00006704518,0.9940743,0.0002763486,0.0002351821,0.0001712645],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916754,0.001563868,0.005454505,0.00003048067,0.001080812,0.00005660148,0.000005423718,0.00006735984,0.00006550357],"genre_scores_gemma":[0.9982139,0.0004143679,0.0007996183,0.00001256599,0.0004855697,0.000003476411,0.000001863146,0.0000355857,0.00003309469],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04185083,"threshold_uncertainty_score":0.7056305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01558797139717067,"score_gpt":0.2513695016664825,"score_spread":0.2357815302693119,"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."}}