{"id":"W4387638547","doi":"10.3390/en16207094","title":"Health Prognostics Classification with Autoencoders for Predictive Maintenance of HVAC Systems","year":2023,"lang":"en","type":"article","venue":"Energies","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Secretaría de Educación Superior, Ciencia, Tecnología e Innovación","keywords":"HVAC; Prognostics; Autoencoder; Reliability engineering; Data-driven; Computer science; Machine learning; Engineering; Artificial intelligence; Turbofan; Data mining; Air conditioning; Artificial neural network; Automotive engineering","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":[0.00008241147,0.00006477178,0.0001075678,0.0000643338,0.00004054998,0.000009821633,0.000056515,0.00003280114,3.510146e-7],"category_scores_gemma":[0.00001413537,0.00005516498,0.0000156638,0.0002158957,0.00002388334,0.00006200766,0.000005580188,0.00003030237,3.23036e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003504027,"about_ca_system_score_gemma":0.00003053428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000208033,"about_ca_topic_score_gemma":0.00001175864,"domain_scores_codex":[0.999569,0.000007854829,0.0001286976,0.00008194951,0.00007978224,0.0001327175],"domain_scores_gemma":[0.9997362,0.00004650664,0.00004739427,0.00009798932,0.00005157675,0.00002036365],"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":[0.00001126921,0.000004475097,0.0002204832,0.0001445789,0.000024003,1.404916e-7,0.0001763377,0.9827635,0.0000505407,0.01241675,0.00376867,0.0004192019],"study_design_scores_gemma":[0.000141743,0.00008386708,0.001244763,0.0001060782,0.000006004354,8.380501e-7,0.0003955684,0.9960282,0.0003687468,0.0001085189,0.001453655,0.00006203086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1391167,0.0006187009,0.8554422,0.0003472787,0.0008441203,0.0005841726,0.00007771549,0.001734517,0.001234572],"genre_scores_gemma":[0.994433,0.0002108559,0.004720238,0.000005871359,0.00003571302,0.0001687454,0.00007784806,0.00002234014,0.0003254239],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8553163,"threshold_uncertainty_score":0.2249562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0151955362715385,"score_gpt":0.2213404200817448,"score_spread":0.2061448838102063,"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."}}