{"id":"W2162399951","doi":"10.3390/s100100241","title":"A Multi-Fault Diagnosis Method for Sensor Systems Based on Principle Component Analysis","year":2009,"lang":"en","type":"article","venue":"Sensors","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Shanghai Municipal Education Commission; National Natural Science Foundation of China","keywords":"Principal component analysis; Fault (geology); Artificial neural network; Component (thermodynamics); Fault detection and isolation; Computer science; Soft sensor; Mean squared error; Pattern recognition (psychology); Engineering; Artificial intelligence; Algorithm; Mathematics; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0003202996,0.0002403652,0.0004902197,0.0003609978,0.00009108108,0.00006599398,0.0001051986,0.0001257571,0.00001394162],"category_scores_gemma":[0.00006952806,0.000221656,0.0003598587,0.0004855232,0.00000786616,0.00002736074,0.000003376962,0.0001217153,0.00004508669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001334149,"about_ca_system_score_gemma":0.000006473941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009531163,"about_ca_topic_score_gemma":0.00003790363,"domain_scores_codex":[0.9986285,0.000129071,0.0003780239,0.0003001984,0.0002332141,0.0003309998],"domain_scores_gemma":[0.9990571,0.0002897076,0.00006473946,0.0003831592,0.00006723413,0.000138123],"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":[0.00003731529,0.00005281093,0.0002168761,0.00004773369,0.0002736332,0.000005925119,0.00006660334,0.9938972,0.003515648,0.00003649278,0.0001859811,0.001663761],"study_design_scores_gemma":[0.001085959,0.0001011634,0.002272535,0.00002699459,0.0002346102,0.000002502526,0.0001062192,0.9681222,0.001976831,0.000001256645,0.02581366,0.0002559917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.477647,0.0002640721,0.5148535,0.0004516269,0.001499136,0.002232069,0.000218103,0.001646678,0.001187766],"genre_scores_gemma":[0.9874557,0.000006205832,0.01157442,0.0001451668,0.0001369708,0.0002122237,0.00002164774,0.00003441449,0.0004132763],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5098086,"threshold_uncertainty_score":0.9038869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01765621879095864,"score_gpt":0.2834185802252999,"score_spread":0.2657623614343413,"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."}}