{"id":"W2517450510","doi":"10.1016/j.enbuild.2016.08.083","title":"PCA-based method of soft fault detection and identification for the ongoing commissioning of chillers","year":2016,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Outlier; HVAC; Identification (biology); Anomaly detection; Principal component analysis; Project commissioning; Data mining; Engineering; Computer science; Data set; Fault (geology); Fault detection and isolation; Set (abstract data type); Training set; Reliability engineering; Artificial intelligence; Air conditioning","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":[0.000341289,0.0006315043,0.0006261205,0.001242888,0.0004290129,0.0005764735,0.000563395,0.0004643938,0.00166389],"category_scores_gemma":[0.001033893,0.0002173153,0.0005608046,0.0008349224,0.0002400024,0.0004623585,0.0003484586,0.0006354238,0.0007792231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002725238,"about_ca_system_score_gemma":0.0007458623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004973257,"about_ca_topic_score_gemma":0.005620894,"domain_scores_codex":[0.9996065,0.00005329098,0.00001894165,0.00008831512,0.0001862399,0.0000466272],"domain_scores_gemma":[0.9995754,0.0001043446,0.0000400599,0.00004034484,0.0002207617,0.00001902584],"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":[0.000452278,0.000194363,0.002831273,0.0002417939,0.00009854096,0.0001474391,0.000101005,0.05669836,0.0727773,0.002205414,0.004560825,0.8596914],"study_design_scores_gemma":[0.0000123717,0.00008507627,0.007319265,0.000009503346,0.00004319118,0.0001581531,0.00002479111,0.9674298,0.02215129,0.0006537749,0.002080608,0.00003211793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03487378,0.0004973874,0.9613059,0.00008511419,0.0001073849,0.00004257234,0.0001427786,0.001524803,0.001420287],"genre_scores_gemma":[0.7313904,0.0007187452,0.2618543,0.00007788965,0.0001310275,0.0001061877,0.0005261986,0.0001272417,0.005068091],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004973257,"threshold_uncertainty_score":0.009888649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006659736379484096,"score_gpt":0.2237514488798414,"score_spread":0.2170917125003573,"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."}}