{"id":"W4409737878","doi":"10.1016/j.aei.2025.103361","title":"Prognostics of complex machinery with sparse multilabel multimodal run-to-failure data: A graph neural network approach","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hydro-Québec","funders":"Région Occitanie Pyrénées-Méditerranée; Ecole Nationale d'Ingénieurs de Tunis","keywords":"Prognostics; Artificial neural network; Computer science; Artificial intelligence; Graph; Machine learning; Data mining; Pattern recognition (psychology); Theoretical computer science","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":[0.0005597333,0.0009810602,0.0009918205,0.0009449085,0.000293117,0.0006826008,0.0009725129,0.001163112,0.001103924],"category_scores_gemma":[0.00263573,0.0004420627,0.0005939105,0.000627148,0.0005480507,0.00118903,0.0007537829,0.001015344,0.0002365024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004736561,"about_ca_system_score_gemma":0.000586077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008000349,"about_ca_topic_score_gemma":0.009758906,"domain_scores_codex":[0.9998203,0.00004486349,0.00001012388,0.00005799575,0.00003590141,0.00003083623],"domain_scores_gemma":[0.999212,0.0004408685,0.000130243,0.00006322176,0.0001102467,0.00004337678],"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":[0.0001020585,0.00004475448,0.001644201,0.0000428075,0.0000393541,0.00007285955,0.0000236578,0.9690517,0.001174328,0.0008951905,0.0006034235,0.02630574],"study_design_scores_gemma":[0.000001379026,0.000006986218,0.0002702466,0.000001518759,0.000003179009,0.00000453428,0.00000304678,0.9985976,0.0001106999,0.000971541,0.00002671178,0.000002612105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1749983,0.0007191972,0.8203209,0.0007275143,0.0001166511,0.00005938778,0.0005024534,0.001184521,0.001371065],"genre_scores_gemma":[0.9811572,0.0001619297,0.01713218,0.00006938415,0.00005112799,0.00002905904,0.0003267986,0.00003182982,0.001040456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008000349,"threshold_uncertainty_score":0.01590753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01117978992688429,"score_gpt":0.2509937024499,"score_spread":0.2398139125230157,"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."}}