{"id":"W4416919401","doi":"10.1016/j.neucom.2025.132252","title":"A modular deep learning methodology for multi-fault machine health diagnostics from sparse and imbalanced multimodal data","year":2025,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hydro-Québec","funders":"Région Occitanie Pyrénées-Méditerranée; Ecole Nationale d'Ingénieurs de Tunis; École de technologie supérieure","keywords":"Modular design; Scalability; Adaptability; Retraining; Deep learning; Key (lock); Pipeline (software)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006619094,0.0003046115,0.000537611,0.0001603241,0.0002091135,0.00006231213,0.000492994,0.0001262979,0.000003315042],"category_scores_gemma":[0.001910488,0.0003364631,0.00005042688,0.000181776,0.00003639306,0.0001023857,0.0005330463,0.0005596931,0.000001592198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004866902,"about_ca_system_score_gemma":0.00002095818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005696223,"about_ca_topic_score_gemma":0.0001992867,"domain_scores_codex":[0.9980114,0.0002631698,0.0004837438,0.0006553392,0.000102768,0.0004835556],"domain_scores_gemma":[0.9965073,0.002676924,0.0001088667,0.0005589073,0.00004231895,0.0001056906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004066557,0.0001736114,0.06335296,0.0007271399,0.0001947071,0.00002701353,0.0008128547,0.1696093,0.009631369,0.0003224031,0.001800068,0.7533079],"study_design_scores_gemma":[0.000972799,0.0000556644,0.02678777,0.0001151379,0.00003069636,0.000003731437,0.00002120059,0.96457,0.001224463,0.0001695376,0.005822164,0.0002267907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1447314,0.002836059,0.8497902,0.0002665777,0.0003591015,0.000755777,0.0001092516,0.001118737,0.00003292453],"genre_scores_gemma":[0.4766056,0.0005721709,0.5218458,0.0005089655,0.0001090335,0.00005041653,0.0002441832,0.00005800193,0.00000587721],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7949607,"threshold_uncertainty_score":0.9999087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07093757754942441,"score_gpt":0.3757168397921861,"score_spread":0.3047792622427616,"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."}}