{"id":"W4362669277","doi":"10.3390/su15076270","title":"Machine Learning Applications for Reliability Engineering: A Review","year":2023,"lang":"en","type":"review","venue":"Sustainability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; Hydro-Québec; Université du Québec à Trois-Rivières","keywords":"Maintainability; Reliability (semiconductor); Computer science; Prognostics; Big data; Artificial intelligence; Cloud computing; Machine learning; Realization (probability); Reliability engineering; Systems engineering; Software engineering; Engineering; Data mining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001056339,0.001070907,0.001350255,0.003243261,0.0003742715,0.001285527,0.001186729,0.00126545,0.005846645],"category_scores_gemma":[0.002058263,0.0004942102,0.001032964,0.005183636,0.0004033114,0.001909971,0.0007586722,0.001510046,0.002791427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005501945,"about_ca_system_score_gemma":0.001531273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001664093,"about_ca_topic_score_gemma":0.002227648,"domain_scores_codex":[0.9996075,0.00007520274,0.00006561777,0.00006845499,0.00015506,0.00002819263],"domain_scores_gemma":[0.9984453,0.0009577645,0.000131924,0.00004086629,0.0003798729,0.00004423008],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003919163,0.00009844997,0.0003350654,0.02985423,0.0001247359,0.0001459023,0.00009314871,0.001614307,0.0009081247,0.006469962,0.02532474,0.9349921],"study_design_scores_gemma":[0.00001124473,0.0001303775,0.001185059,0.0123733,0.0002460708,0.0008034091,0.00009751489,0.001063336,0.0006821027,0.005027609,0.9783348,0.00004509251],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002068446,0.9962942,0.001128925,0.0002949685,0.0002244017,0.00001597685,0.0000375732,0.00002079918,0.00177621],"genre_scores_gemma":[0.001369351,0.9964644,0.001081989,0.0001480999,0.0002442813,0.00001521804,0.00006246582,0.000005683887,0.0006084665],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005846645,"threshold_uncertainty_score":0.01955897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01881194137560345,"score_gpt":0.3027223972607374,"score_spread":0.2839104558851339,"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."}}