{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007398224,0.00005916851,0.00008775852,0.00009968382,0.00002824598,0.000007216573,0.00002914152,0.00003041589,0.000004392686],"category_scores_gemma":[0.00002932862,0.00006018503,0.00001245708,0.0001396621,0.0000134309,0.000034169,0.00001980375,0.0001095501,5.706573e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002434317,"about_ca_system_score_gemma":0.000003514252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007251065,"about_ca_topic_score_gemma":0.000003066595,"domain_scores_codex":[0.9996356,0.0000166036,0.0001202851,0.00008845051,0.00005205257,0.00008694408],"domain_scores_gemma":[0.9998609,0.00001764413,0.00001718532,0.00005470787,0.0000285743,0.00002103302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001541222,0.00002458095,0.3294503,0.0001754974,0.0001949681,0.000003154213,0.003904908,0.1186331,0.02837995,0.0004306897,0.002410252,0.5162385],"study_design_scores_gemma":[0.0007352141,0.0001580564,0.02765384,0.0002211693,0.00003164984,0.000002426274,0.002101776,0.007719393,0.9522345,0.0004355554,0.008524529,0.0001818762],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979964,0.00009345379,0.000449044,0.0001185046,0.0002530099,0.0001337002,0.000008973711,0.0001987264,0.0007481349],"genre_scores_gemma":[0.9988141,0.00007789701,0.0008511372,0.000003985384,0.00005746448,0.000009764326,0.000002108086,0.000006862919,0.0001766479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9238546,"threshold_uncertainty_score":0.2454274,"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."}}