{"id":"W4386535013","doi":"10.1016/j.jmsy.2023.08.015","title":"A deep learning based sensor fusion method to diagnose tightening errors","year":2023,"lang":"en","type":"article","venue":"Journal of Manufacturing Systems","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Modular design; Encoder; Heuristic; Artificial intelligence; Computer science; Feature (linguistics); Sensor fusion; Feature vector; Data mining; Inference; Machine learning; Process (computing); Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001045509,0.001012395,0.0008554086,0.000826675,0.0003938867,0.0007786208,0.001194475,0.001288102,0.00170731],"category_scores_gemma":[0.00171857,0.0004604103,0.0007635324,0.0005963041,0.0005189298,0.001383006,0.001132807,0.001289621,0.0003555884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009409236,"about_ca_system_score_gemma":0.00104589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002854215,"about_ca_topic_score_gemma":0.004351576,"domain_scores_codex":[0.9993811,0.00009521664,0.00003520054,0.0002050295,0.0002065257,0.00007692472],"domain_scores_gemma":[0.9994609,0.0001321978,0.0001118564,0.00007652848,0.0001855984,0.00003291128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003663653,0.000249538,0.002846916,0.0001253391,0.0001397122,0.0001630328,0.0001181541,0.4763224,0.03071591,0.008234164,0.002979266,0.4777391],"study_design_scores_gemma":[0.000004728384,0.00005229481,0.0002681796,0.000007098186,0.00001097499,0.00002982534,0.00000735976,0.9927174,0.005116746,0.001302521,0.0004752261,0.000007694859],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01456788,0.0002171254,0.9830783,0.0001373645,0.0000355993,0.00003503495,0.00006396639,0.0007049612,0.001159805],"genre_scores_gemma":[0.5887179,0.0001900607,0.4062316,0.0002450279,0.00003792379,0.000108645,0.0002661919,0.00009173354,0.004110963],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002854215,"threshold_uncertainty_score":0.006826878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01291837649815941,"score_gpt":0.2898779109305317,"score_spread":0.2769595344323723,"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."}}