{"id":"W1905427462","doi":"10.1109/imtc.2002.1006949","title":"Combining partial least squares and feed forward neural network technologies in a fault detection system with large number of correlated sensors","year":2003,"lang":"en","type":"article","venue":"","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Kinectrics (Canada)","funders":"","keywords":"Stator; Fault detection and isolation; Artificial neural network; Fault (geology); Electromagnetic coil; Process (computing); Electric power system; Computer science; Field (mathematics); Least-squares function approximation; Electronic engineering; Control engineering; Control theory (sociology); Power (physics); Engineering; Artificial intelligence; Electrical engineering; Mathematics; Physics; 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":[0.0001308877,0.0001276463,0.0002042471,0.00004537718,0.00003009118,0.00001293765,0.00003506924,0.0001309082,0.000002756462],"category_scores_gemma":[0.00006483569,0.000102654,0.00002065986,0.0002580201,0.00003311315,0.00004230832,0.00001306318,0.0001933984,0.000001610507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002674839,"about_ca_system_score_gemma":0.000003652968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003320821,"about_ca_topic_score_gemma":0.00007411848,"domain_scores_codex":[0.9993346,0.00001864679,0.000205083,0.0001252648,0.00007505652,0.0002413695],"domain_scores_gemma":[0.9997085,0.00009226008,0.00002446255,0.0001211187,0.00002737743,0.00002632068],"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":[0.00001310906,0.00001637171,0.1041526,0.0001739893,0.00002448479,0.000007308523,0.0000749783,0.893732,0.0002783731,0.00131926,0.000009921968,0.0001975864],"study_design_scores_gemma":[0.0008327713,0.00007350312,0.0122039,0.0001919371,0.00002003498,0.00004537341,0.001106465,0.9792894,0.005919645,0.0000263565,0.00008599325,0.0002046258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9838869,0.0001356212,0.0144513,0.000005516171,0.0002127609,0.0001628437,0.000002360425,0.0005700056,0.0005727106],"genre_scores_gemma":[0.999314,0.00001963138,0.0006142791,8.074641e-7,0.000006844886,0.0000158081,0.000001246712,0.00002002913,0.000007391396],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09194874,"threshold_uncertainty_score":0.4186109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003292080757679787,"score_gpt":0.1825273811909049,"score_spread":0.1792353004332251,"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."}}