{"id":"W4403111471","doi":"10.11591/ijece.v14i6.pp7211-7223","title":"PdM-FSA: predictive maintenance framework with fault severity awareness in Industry 4.0 using machine learning","year":2024,"lang":"en","type":"article","venue":"International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"University of South Africa","keywords":"Predictive maintenance; Downtime; Machine learning; Computer science; Random forest; Support vector machine; Industry 4.0; Overall equipment effectiveness; Artificial intelligence; Context (archaeology); Fault detection and isolation; Data mining; Reliability engineering; Productivity; Engineering","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":[0.001183343,0.0006262843,0.0008909502,0.001047693,0.0004130489,0.0009460332,0.001630558,0.001035983,0.001084744],"category_scores_gemma":[0.002329726,0.0002491762,0.0007065258,0.0005812351,0.0003903325,0.001121305,0.0008330367,0.0009475952,0.0002951816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008328528,"about_ca_system_score_gemma":0.001365436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009668079,"about_ca_topic_score_gemma":0.01016177,"domain_scores_codex":[0.9995084,0.0001116245,0.00003078424,0.0001297612,0.0001613972,0.00005803674],"domain_scores_gemma":[0.9993151,0.0002699329,0.0001072383,0.00006540517,0.0001858113,0.00005641883],"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":[0.000128134,0.0002094381,0.006380495,0.0001296397,0.00009672566,0.0001773655,0.0001456089,0.7621168,0.002850615,0.009691773,0.004225777,0.2138476],"study_design_scores_gemma":[0.000003987351,0.00002183914,0.0003364905,0.000005658166,0.000009001433,0.00002032313,0.000008058174,0.9964997,0.0002421349,0.002215061,0.0006332222,0.000004600658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02732811,0.0006309617,0.9669038,0.0003241949,0.00006305818,0.00009482201,0.0002041358,0.002871911,0.001579048],"genre_scores_gemma":[0.7877524,0.0003683984,0.209379,0.0001505513,0.0000932737,0.0001741124,0.0005222756,0.00009065486,0.001469245],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009668079,"threshold_uncertainty_score":0.01922363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005222010152297066,"score_gpt":0.2244748709424952,"score_spread":0.2192528607901981,"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."}}