{"id":"W4362635366","doi":"10.1007/s10845-023-02118-z","title":"Root cause analysis of an out-of-control process using a logical analysis of data regression model and exponential weighted moving average","year":2023,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"EWMA chart; Control chart; Anomaly detection; Data mining; Constant false alarm rate; Scatter plot; Regression analysis; Root cause analysis; Computer science; Multivariate statistics; Process (computing); Statistical process control; Artificial intelligence; Statistics; Engineering; Machine learning; Mathematics; Reliability engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002967578,0.0002420844,0.001517118,0.003218831,0.0001123149,0.00008710784,0.001244003,0.0001115773,0.00007240618],"category_scores_gemma":[0.001960517,0.0001644005,0.0003522612,0.002300506,0.000146206,0.001220639,0.0004009587,0.0003249375,0.000001185795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005980452,"about_ca_system_score_gemma":0.00007892437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001887223,"about_ca_topic_score_gemma":0.00005789345,"domain_scores_codex":[0.9943844,0.0002166755,0.002479612,0.0005385196,0.002075092,0.0003056684],"domain_scores_gemma":[0.994066,0.00157589,0.002657443,0.000742244,0.0007268563,0.0002315441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003916503,0.0001168959,0.009834675,0.00005386935,0.002380551,0.00006553924,0.001300326,0.9592741,0.00769385,0.00001497741,0.000002671234,0.01887086],"study_design_scores_gemma":[0.0002585794,0.00007379789,0.008844866,0.0001226557,0.003632057,0.00000379067,0.0008923019,0.926621,0.05652672,0.00287359,0.000002716952,0.0001479193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5534192,0.00006966361,0.4462683,0.00001069137,0.00008658899,0.00005205043,0.00008203599,0.00000621544,0.000005287459],"genre_scores_gemma":[0.9885251,0.00005428302,0.01131618,0.000004604414,0.00005780349,7.093697e-7,0.00001450165,0.00001489827,0.00001193802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4351059,"threshold_uncertainty_score":0.6704056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2482077620827757,"score_gpt":0.467874391981397,"score_spread":0.2196666298986212,"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."}}