{"id":"W1739018821","doi":"10.1109/cca.1996.559050","title":"A neural network application to fault diagnosis for robotic manipulator","year":2002,"lang":"en","type":"article","venue":"","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Ryerson University","keywords":"Artificial neural network; Fault detection and isolation; Residual; Computer science; Set (abstract data type); Robot manipulator; Feedforward neural network; Control engineering; Artificial intelligence; Time delay neural network; Fault (geology); Feed forward; Probabilistic neural network; Control theory (sociology); Robot; Engineering; Control (management); Actuator; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003411725,0.00008496232,0.0001089684,0.00002864631,0.00004379261,0.00002953043,0.00006649501,0.000043424,0.0000860451],"category_scores_gemma":[0.000007982962,0.00008111113,0.00005155888,0.0001376921,0.000002233526,0.00004121288,0.000005257949,0.00003487002,0.0002791006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003304828,"about_ca_system_score_gemma":4.105476e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001848574,"about_ca_topic_score_gemma":0.00007040718,"domain_scores_codex":[0.9994905,0.000005529897,0.0001412122,0.000114486,0.00006024435,0.0001880398],"domain_scores_gemma":[0.9997219,0.00003456425,0.000009833338,0.0001405535,0.00001562913,0.00007752109],"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":[0.000002170218,0.000006667676,0.0002477567,0.00001852539,0.00001291986,1.707217e-7,0.00002713889,0.9492722,0.0002915591,0.0003236618,0.02961513,0.0201821],"study_design_scores_gemma":[0.0001808982,0.00002632554,0.0003508285,0.000004571328,0.000007260959,0.000002026086,0.00001171981,0.9413462,0.0001233081,0.0000221835,0.05782227,0.0001023677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03776234,0.0006049537,0.948999,0.0009611654,0.001563904,0.002092824,0.000002844908,0.001552965,0.006459998],"genre_scores_gemma":[0.9966052,0.000005780092,0.0005324883,0.0002904696,0.0003692197,0.001316901,0.000001590835,0.00002368112,0.0008547023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9588428,"threshold_uncertainty_score":0.3587367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01581347197435766,"score_gpt":0.2182098864441323,"score_spread":0.2023964144697747,"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."}}