{"id":"W4205843111","doi":"10.1109/tii.2021.3134250","title":"Development of an Explainable Fault Diagnosis Framework Based on Sensor Data Imagification: A Case Study of the Robotic Spot-Welding Process","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Welding Techniques and Residual Stresses","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Research Foundation of Korea","keywords":"Interpretability; Computer science; Artificial intelligence; Fault (geology); Data mining; Machine learning; Process (computing); Convolutional neural network; Field (mathematics); Fault detection and isolation; Inference; Pattern recognition (psychology)","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":[0.000924607,0.0006532671,0.0003962005,0.000687841,0.0004551148,0.0007196863,0.0008983056,0.001278839,0.001269187],"category_scores_gemma":[0.001778068,0.000235063,0.0006513005,0.0003263272,0.0007999689,0.0008972407,0.0006498669,0.00073339,0.0000897138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008710208,"about_ca_system_score_gemma":0.0007740065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006673323,"about_ca_topic_score_gemma":0.00663843,"domain_scores_codex":[0.9996408,0.0001146869,0.00002109464,0.00007422848,0.0001181727,0.00003091242],"domain_scores_gemma":[0.9992282,0.0004937804,0.00007746409,0.00006918124,0.0001100514,0.00002130452],"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":[0.0002927768,0.0002514352,0.01228835,0.0004990572,0.0001102611,0.007011863,0.001385207,0.8041918,0.02836972,0.03750465,0.001230646,0.1068642],"study_design_scores_gemma":[0.00002017132,0.00006838587,0.001448388,0.00001781152,0.00002060719,0.0004098322,0.0001966843,0.9786851,0.009387081,0.008194397,0.001532209,0.00001923502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2522134,0.0005105999,0.7411626,0.0009202455,0.00002845807,0.0002137841,0.0002352379,0.0005320683,0.00418372],"genre_scores_gemma":[0.8804766,0.00019078,0.1181082,0.0000328724,0.000008343488,0.00005579627,0.000110391,0.00002108111,0.0009959912],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006673323,"threshold_uncertainty_score":0.01326901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0951140719468133,"score_gpt":0.3104665422492357,"score_spread":0.2153524703024224,"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."}}