{"id":"W4407293267","doi":"10.3390/s25041006","title":"A Machine Learning Implementation to Predictive Maintenance and Monitoring of Industrial Compressors","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Oil and Gas Production Techniques","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières; Cegep de Sept Iles","funders":"Natural Sciences and Engineering Research Council of Canada; Université du Québec à Trois-Rivières","keywords":"Predictive maintenance; Computer science; Cloud computing; Software deployment; Metric (unit); Data mining; Machine learning; Warning system; Data collection; Data acquisition; Real-time computing; Reliability engineering; Engineering; Artificial intelligence","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":[0.000460575,0.0005562415,0.000407753,0.0004950971,0.0003449574,0.0005303728,0.001259585,0.0005718379,0.002573699],"category_scores_gemma":[0.001838243,0.0002863508,0.0003332457,0.0005209725,0.0002425471,0.0007054698,0.0004799991,0.0007505285,0.0007245291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004478475,"about_ca_system_score_gemma":0.0007065564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005094281,"about_ca_topic_score_gemma":0.003963266,"domain_scores_codex":[0.999658,0.00004542866,0.00002659841,0.0001165741,0.0001218656,0.00003157555],"domain_scores_gemma":[0.999557,0.0001464863,0.00003833884,0.00008864877,0.0001470105,0.00002242893],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005760958,0.0007684025,0.01033046,0.0002482773,0.0001846357,0.000640969,0.0002638045,0.2515997,0.0382924,0.008455339,0.01183637,0.6768036],"study_design_scores_gemma":[0.00002234583,0.00006626144,0.001004638,0.00001093405,0.00001204059,0.00006307047,0.00001770978,0.9832234,0.01115614,0.00135014,0.003062861,0.00001052342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05165443,0.000153874,0.9146504,0.0002850226,0.0001219466,0.0002199703,0.0003696305,0.02900255,0.003542166],"genre_scores_gemma":[0.7241651,0.00015351,0.2708662,0.0001970118,0.00005548131,0.0002200764,0.0006881938,0.0002188111,0.003435533],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005094281,"threshold_uncertainty_score":0.01012927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01361018984141592,"score_gpt":0.2671491560154782,"score_spread":0.2535389661740622,"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."}}