{"id":"W4381192996","doi":"10.32920/23541828","title":"Fault Detection and Diagnosis for Central Heating System Using Equipment Emulators and Vibration Monitoring Techniques","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Sciencetech (Canada)","funders":"","keywords":"HVAC; Fault detection and isolation; Leverage (statistics); Automation; Engineering; Vibration; Boiler (water heating); Condition monitoring; Reliability engineering; Computer science; Real-time computing; Control engineering; Artificial intelligence; Mechanical engineering; Electrical engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001694617,0.0002498809,0.0002447581,0.0001505587,0.0001635903,0.0001812307,0.00004762657,0.0002446515,5.517641e-7],"category_scores_gemma":[0.00002346503,0.0002644957,0.00005120564,0.00006883445,0.00001093485,0.0001225884,0.0001440378,0.0001954795,1.408598e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002483389,"about_ca_system_score_gemma":0.000009035961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003182114,"about_ca_topic_score_gemma":0.00003626425,"domain_scores_codex":[0.9989688,0.00001579244,0.000321792,0.0002981942,0.0001071201,0.0002883334],"domain_scores_gemma":[0.9996101,0.0001010475,0.00006627147,0.0001144629,0.00003135647,0.00007682366],"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":[0.00001544459,0.00001158432,0.02916722,0.009235906,0.0002685988,0.000006241555,0.0016147,0.8031919,0.04465535,0.0002849599,0.00001384338,0.1115342],"study_design_scores_gemma":[0.00008542494,0.00002283555,0.0007697503,0.001849622,0.00004994851,0.000005994521,0.0003164998,0.7894275,0.2070781,0.00006714472,0.00003730207,0.0002898942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7866375,0.0003698668,0.2095186,0.000005083218,0.001504852,0.0004592504,0.00001361454,0.001405399,0.00008589276],"genre_scores_gemma":[0.9755092,0.0001896135,0.02337278,0.000001509723,0.0005604392,0.0002681037,0.00001182816,0.00007705778,0.000009476385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1888717,"threshold_uncertainty_score":0.9999807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03574703273348581,"score_gpt":0.2632345792151601,"score_spread":0.2274875464816743,"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."}}