{"id":"W4393133260","doi":"10.2139/ssrn.4770739","title":"Autoencoder-Based Fault Detection Using Building Automation System Data","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Autoencoder; Fault detection and isolation; Automation; Fault (geology); Computer science; Building automation; Artificial intelligence; Real-time computing; Embedded system; Reliability engineering; Engineering; Deep learning; Seismology; Geology; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003435749,0.0006591936,0.0006269989,0.0006948973,0.0001924753,0.0004720985,0.0003703227,0.000504006,0.001168164],"category_scores_gemma":[0.001362596,0.000248179,0.0003887142,0.0005241897,0.0002096493,0.0005096163,0.0003700091,0.0007695195,0.0004973078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000332129,"about_ca_system_score_gemma":0.0005121429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008248821,"about_ca_topic_score_gemma":0.006809266,"domain_scores_codex":[0.999723,0.00003404605,0.00001832448,0.00008655842,0.00008550984,0.00005242828],"domain_scores_gemma":[0.9994924,0.0002312759,0.00005405582,0.00006525727,0.0001445426,0.00001246041],"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":[0.0005016067,0.0001965014,0.00437679,0.0001276709,0.00009640543,0.0001474582,0.00007242501,0.4395694,0.02812956,0.001059863,0.001734299,0.5239879],"study_design_scores_gemma":[0.000004074959,0.00002729617,0.002255935,0.000006279335,0.00001129719,0.00002737412,0.00000562393,0.9915674,0.005535438,0.0002967526,0.000257713,0.000004802102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2477231,0.0004775663,0.7455087,0.0001950545,0.0001533474,0.00005368037,0.0004742255,0.002747897,0.002666559],"genre_scores_gemma":[0.948285,0.0001209765,0.04918181,0.00003504952,0.0000335334,0.00002412981,0.0005341629,0.00004677583,0.001738528],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008248821,"threshold_uncertainty_score":0.01640159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01793185251820655,"score_gpt":0.2619831810137921,"score_spread":0.2440513284955856,"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."}}