{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005614217,0.0003922455,0.0004708612,0.000875713,0.0002194893,0.0005082515,0.0004267834,0.0004085962,0.001021438],"category_scores_gemma":[0.002498039,0.0001293328,0.0001939827,0.0006822031,0.0002793038,0.0006194258,0.0002594967,0.0003325618,0.000387603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002653525,"about_ca_system_score_gemma":0.0001924351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009248542,"about_ca_topic_score_gemma":0.0008176651,"domain_scores_codex":[0.9991296,0.0001526716,0.00003521528,0.0001888653,0.0004300107,0.00006368265],"domain_scores_gemma":[0.9985317,0.000560536,0.0002562604,0.0002959276,0.0003237329,0.00003175517],"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":[0.0008541172,0.0002140819,0.01669322,0.0003574262,0.00004665508,0.0002173861,0.0004252206,0.01912525,0.5500889,0.0009212343,0.000828371,0.410228],"study_design_scores_gemma":[0.00006429727,0.0009559026,0.1015176,0.00005409063,0.00009138085,0.0009422573,0.0001898082,0.3961309,0.4940236,0.001862656,0.004094404,0.00007296329],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4903474,0.0006092552,0.5030447,0.0001141976,0.00007580789,0.00008334193,0.000243455,0.00293767,0.002544194],"genre_scores_gemma":[0.9640616,0.0001396271,0.03496519,0.00002287877,0.00002727519,0.00002367747,0.0001240474,0.00005652979,0.0005791986],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001021438,"threshold_uncertainty_score":0.003417075,"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."}}