{"id":"W4315640836","doi":"10.18280/isi.270618","title":"Predictive Maintenance of Electromechanical Systems Using Deep Learning Algorithms: Review","year":2022,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Implementation; Stator; Context (archaeology); Algorithm; Predictive maintenance; Fault (geology); Computer science; Machine learning; Artificial intelligence; Engineering; Control engineering; Reliability engineering; Mechanical engineering; Software engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006415179,0.001016029,0.0008452918,0.001203888,0.00015667,0.0008342409,0.001243492,0.001030559,0.001772163],"category_scores_gemma":[0.002173548,0.000405924,0.0007270233,0.001704782,0.0002908644,0.001455552,0.0005066678,0.0007674709,0.0006754533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003717985,"about_ca_system_score_gemma":0.0008589583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001997118,"about_ca_topic_score_gemma":0.001466062,"domain_scores_codex":[0.9997426,0.00003846345,0.00003950045,0.00006978194,0.00009029826,0.00001939409],"domain_scores_gemma":[0.9989169,0.0007255957,0.00008986722,0.00003476247,0.0002089179,0.00002404562],"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":[0.00005718795,0.00006888,0.0008158882,0.01174444,0.0002001498,0.0001019825,0.00005272054,0.01421124,0.0008048646,0.004296463,0.007948595,0.9596975],"study_design_scores_gemma":[0.00006469064,0.0009553557,0.007895159,0.01935039,0.001858314,0.002541046,0.0003109542,0.1294681,0.01030648,0.02459434,0.8024291,0.0002259823],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001747266,0.9828464,0.01249416,0.0004026673,0.0002607112,0.0000149469,0.00005331741,0.00006515463,0.002115251],"genre_scores_gemma":[0.02346427,0.9691001,0.005465966,0.0001963494,0.0004674355,0.0000207642,0.0001642555,0.00001798636,0.001102882],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.001997118,"threshold_uncertainty_score":0.005928457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009642018061312452,"score_gpt":0.2451711455158906,"score_spread":0.2355291274545781,"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."}}