{"id":"W4291366336","doi":"10.3390/app12168081","title":"On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges","year":2022,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Quality and Safety in Healthcare","field":"Health Professions","cited_by":466,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cegep de Sept Iles; Université du Québec à Trois-Rivières; Université du Québec à Rimouski","funders":"","keywords":"Predictive maintenance; Prognostics; Downtime; Workflow; Industry 4.0; Condition-based maintenance; Context (archaeology); Predictive analytics; Engineering; Risk analysis (engineering); Production (economics); Computer science; Reliability engineering; Data science; Data mining; Business","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":[0.003270462,0.001758084,0.00149282,0.004628277,0.0005937149,0.004399257,0.002823488,0.002847761,0.002172019],"category_scores_gemma":[0.005693536,0.0009293801,0.001301215,0.005471524,0.001530398,0.005605458,0.001893246,0.003337513,0.001178888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002195089,"about_ca_system_score_gemma":0.00215245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006675432,"about_ca_topic_score_gemma":0.003535149,"domain_scores_codex":[0.9983312,0.0004803455,0.0001506752,0.0002817677,0.0006491291,0.0001069293],"domain_scores_gemma":[0.9942252,0.004343433,0.0003396595,0.0002135318,0.0007586164,0.0001195168],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009543127,0.0002497067,0.005813176,0.00567446,0.0002025965,0.0003957118,0.000568025,0.2052825,0.001307672,0.2922002,0.01970992,0.4685005],"study_design_scores_gemma":[0.00001626806,0.0002598031,0.002466328,0.003619635,0.0001430338,0.000592301,0.000468215,0.5495012,0.001498351,0.2349812,0.2062844,0.0001691237],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.008267512,0.4338748,0.5216185,0.009618092,0.001108976,0.0001700439,0.0006075184,0.001132187,0.02360233],"genre_scores_gemma":[0.191379,0.6104575,0.1842304,0.001771688,0.003755561,0.0004063689,0.001598073,0.0002945139,0.006106999],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.006675432,"threshold_uncertainty_score":0.01729608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3400403376815759,"score_gpt":0.4494295485135332,"score_spread":0.1093892108319573,"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."}}