{"id":"W3139037155","doi":"10.1109/tii.2020.3031496","title":"Principal Component Analysis-Based Ensemble Detector for Incipient Faults in Dynamic Processes","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Principal component analysis; Detector; Residual; Fault detection and isolation; Computer science; Linear subspace; Pattern recognition (psychology); Row; Fault (geology); Artificial intelligence; Algorithm; Mathematics; Actuator","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":[0.0001441212,0.0002179942,0.0003811137,0.0003830209,0.00008945436,0.00005280838,0.0001507575,0.000215991,0.00002077022],"category_scores_gemma":[0.00002786184,0.0002182141,0.0001727644,0.001219088,0.00001870409,0.0001762957,7.298489e-7,0.0003803889,0.00002801636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002195604,"about_ca_system_score_gemma":0.00009675024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002402055,"about_ca_topic_score_gemma":0.0003799669,"domain_scores_codex":[0.9985416,0.00002413552,0.0007893908,0.0001151217,0.0002478943,0.0002818774],"domain_scores_gemma":[0.9993666,0.0001493012,0.00009699408,0.0001618938,0.0000678998,0.0001572611],"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":[0.0002195379,0.00004684483,0.00001217254,0.0001885432,0.000182954,6.907983e-7,0.0008284617,0.9929232,0.0005818159,0.000001236994,0.00003183172,0.004982735],"study_design_scores_gemma":[0.002813888,0.0002190596,0.00001444438,0.0000406993,0.0001503321,8.441072e-7,0.0004399556,0.9775944,0.01578383,0.000001102021,0.002710052,0.000231382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3067273,0.000007903978,0.6913343,0.00007871489,0.0005674,0.000778825,0.0001309227,0.0002763365,0.00009833399],"genre_scores_gemma":[0.9991049,0.000004316375,0.0003561899,0.0001329281,0.00005654763,0.0002890966,0.00001748085,0.00002296541,0.00001557726],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6923776,"threshold_uncertainty_score":0.8898515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03441339690122424,"score_gpt":0.2487801112990976,"score_spread":0.2143667143978733,"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."}}