{"id":"W2160851474","doi":"10.1109/tim.2007.909411","title":"Modular Neural Network Architecture for Precise Condition Monitoring","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Modular design; Condition monitoring; Artificial neural network; Process (computing); Reliability engineering; Machining; Fault detection and isolation; Fault (geology); Engineering; Computer science; Machine tool; Embedded system; Real-time computing; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003832523,0.0004559826,0.0002656124,0.0003234738,0.0001524809,0.0004010939,0.0007027746,0.0006248245,0.001938043],"category_scores_gemma":[0.0008089326,0.0001667175,0.0002197232,0.0003611744,0.0001813417,0.0005391231,0.000337513,0.0005096323,0.0005477455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003906264,"about_ca_system_score_gemma":0.0003482215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002959369,"about_ca_topic_score_gemma":0.003375327,"domain_scores_codex":[0.9998202,0.00003367502,0.00001063928,0.00005441767,0.00006069856,0.00002048355],"domain_scores_gemma":[0.9997819,0.00005695911,0.00002906264,0.00003391266,0.00008995054,0.000008129466],"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":[0.0002084678,0.00008831083,0.0009956595,0.000117697,0.00006358631,0.000103051,0.00005397488,0.4994833,0.04325131,0.007068341,0.002690022,0.4458763],"study_design_scores_gemma":[0.000008280573,0.00005508425,0.0004113943,0.000006210817,0.00001141716,0.0000264221,0.000002750134,0.9935481,0.00330705,0.001481956,0.001134384,0.000007062137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02424964,0.0005733153,0.9697254,0.0001151331,0.00007484114,0.00003868659,0.00007149174,0.001882105,0.003269438],"genre_scores_gemma":[0.7767081,0.0004127935,0.2170315,0.0001186123,0.00007215238,0.0001617474,0.0002105452,0.00004157402,0.005243054],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002959369,"threshold_uncertainty_score":0.006483436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02981804194542728,"score_gpt":0.2336575784698234,"score_spread":0.2038395365243961,"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."}}