{"id":"W4388478244","doi":"10.13052/ijfp1439-9776.2442","title":"Facilitating Energy Monitoring and Fault Diagnosis of Pneumatic Cylinders with Exergy and Machine Learning","year":2023,"lang":"en","type":"article","venue":"International Journal of Fluid Power","topic":"Refrigeration and Air Conditioning Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Exergy; Pneumatic cylinder; Fault (geology); Engineering; Fault detection and isolation; Pneumatics; Energy (signal processing); Control engineering; Artificial intelligence; Process engineering; Cylinder; Computer science; Mechanical engineering; Actuator","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":[0.0001119553,0.00007335722,0.000117317,0.0002610608,0.00003056062,0.00004162559,0.00008871571,0.00003294959,0.00001507347],"category_scores_gemma":[0.0001429364,0.0000605671,0.00002449301,0.00008891592,0.00003932512,0.0001748809,0.00002617728,0.0001157668,8.434585e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000207485,"about_ca_system_score_gemma":0.000007179755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007324688,"about_ca_topic_score_gemma":0.000002060159,"domain_scores_codex":[0.9994057,0.00001339277,0.0002395747,0.0000541499,0.0002154421,0.00007174419],"domain_scores_gemma":[0.999551,0.0001745958,0.00008517603,0.00003479042,0.0001269707,0.00002744131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001570872,0.00008602825,0.2462217,0.0002003537,0.001939858,0.0003875637,0.01021027,0.4170982,0.118011,0.005806429,0.001364155,0.1985174],"study_design_scores_gemma":[0.007357108,0.002121665,0.1591904,0.004842048,0.0001604761,0.001370204,0.02565557,0.3458546,0.4269713,0.006192691,0.01865621,0.001627735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950099,0.0006053508,0.003573094,0.0002453604,0.0002507836,0.00001467452,0.000004094815,0.00007596285,0.0002207733],"genre_scores_gemma":[0.9975649,0.001270946,0.001075327,0.000005868306,0.00002642299,0.000003367474,0.000002628231,0.000009928985,0.00004067637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3089603,"threshold_uncertainty_score":0.2469855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01134031648450248,"score_gpt":0.2317504594659901,"score_spread":0.2204101429814876,"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."}}