{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000841509,0.0007055289,0.0004690675,0.0004342553,0.0002410116,0.000445557,0.0005772843,0.0005903828,0.0005846851],"category_scores_gemma":[0.001863315,0.0002766491,0.0005182002,0.0002141512,0.0002812082,0.000513377,0.0005186677,0.001080115,0.0002110277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005467586,"about_ca_system_score_gemma":0.0005577875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005958532,"about_ca_topic_score_gemma":0.004988214,"domain_scores_codex":[0.9997079,0.0000640609,0.00002279783,0.00007813401,0.00008816367,0.00003893859],"domain_scores_gemma":[0.9993118,0.0003184823,0.00007770884,0.00005535712,0.0002176673,0.00001903244],"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":[0.0001972265,0.0001600852,0.004401952,0.00007103804,0.00007863532,0.0001038649,0.00008310441,0.7577699,0.0100575,0.0008999567,0.001024391,0.2251523],"study_design_scores_gemma":[0.000001887524,0.00002150226,0.0006903994,0.000004503972,0.000007086575,0.00001030288,0.00000491222,0.9973571,0.001555731,0.0002367718,0.0001068383,0.000002967553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2086281,0.001148019,0.7853898,0.0004194147,0.0001485393,0.00007776823,0.0001666237,0.00143814,0.002583508],"genre_scores_gemma":[0.9484239,0.0002114225,0.04960958,0.0000938478,0.00003991822,0.0000481278,0.0002471617,0.00002362264,0.001302427],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005958532,"threshold_uncertainty_score":0.01184767,"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."}}