{"id":"W4318484885","doi":"10.3390/app13031790","title":"Predictive Maintenance and Fault Monitoring Enabled by Machine Learning: Experimental Analysis of a TA-48 Multistage Centrifugal Plant Compressor","year":2023,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cegep de Sept Iles; Université du Québec à Trois-Rivières; Université du Québec à Rimouski","funders":"","keywords":"Downtime; Predictive maintenance; Overall equipment effectiveness; Computer science; Production (economics); Workflow; Reliability engineering; 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":[0.0003142382,0.0001762727,0.0003258221,0.000328162,0.00015975,0.00005416509,0.0002679638,0.00005464748,0.00002224694],"category_scores_gemma":[0.00003029852,0.00015522,0.00005064361,0.001176552,0.0002164025,0.0001052797,0.0001048591,0.0001607723,0.00000374761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004058615,"about_ca_system_score_gemma":0.000006975721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001798035,"about_ca_topic_score_gemma":0.00001094118,"domain_scores_codex":[0.9987161,0.00002337395,0.0002499112,0.0003245157,0.0003462736,0.0003398678],"domain_scores_gemma":[0.9995446,0.0001613386,0.00007410111,0.0001230029,0.00001614338,0.0000808142],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004986693,0.0001276972,0.1116007,0.00009951593,0.0005203563,0.00001583013,0.003452978,0.1083932,0.7697676,0.0005961272,0.004299612,0.001076475],"study_design_scores_gemma":[0.0002969699,0.00005435032,0.0070076,0.00002471817,0.00007063014,8.389705e-7,0.0009248744,0.5649952,0.4252501,0.00001744717,0.001155208,0.0002019733],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950331,0.0006354896,0.001330594,0.00001564451,0.00008568312,0.0002825545,0.0002141194,0.0006718027,0.001731044],"genre_scores_gemma":[0.9984208,0.0002567176,0.00106274,0.000005387361,0.00001769991,0.0001186969,0.00004948324,0.00001451062,0.00005397635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.456602,"threshold_uncertainty_score":0.632969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01193501659444845,"score_gpt":0.2688826864552332,"score_spread":0.2569476698607848,"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."}}