{"id":"W4407920613","doi":"10.18280/jesa.580103","title":"A Data-Driven Predictive Maintenance Approach for Industry 4.0 Using LSTM with Cross-Validation and the IDAIC Framework","year":2025,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Predictive maintenance; Computer science; Cross-validation; Artificial intelligence; Machine learning; Data mining; Reliability engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001118357,0.0002207426,0.0003712842,0.0001633679,0.0005104448,0.0006513895,0.0003074093,0.0002986621,0.000005686486],"category_scores_gemma":[0.0004109658,0.0001374433,0.00006531517,0.0004422125,0.0001933243,0.0005268479,0.0000888329,0.0008415986,0.000001322782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001859081,"about_ca_system_score_gemma":0.00009147919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001923281,"about_ca_topic_score_gemma":0.000001755315,"domain_scores_codex":[0.99845,0.0002079069,0.0005329734,0.0002532642,0.0002616726,0.0002942171],"domain_scores_gemma":[0.9987881,0.0002921576,0.0002399083,0.0003933584,0.0002100005,0.00007654483],"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":[0.001422659,0.00007796131,0.007513825,0.001473313,0.001676662,0.00002930403,0.002031833,0.8773862,0.0008587814,0.006099949,0.005418802,0.09601077],"study_design_scores_gemma":[0.002105164,0.0000923683,0.01658914,0.001073006,0.0001561372,0.0005727554,0.0004431849,0.9764277,0.0001512853,0.001582564,0.0006172128,0.0001895002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2728692,0.0005096065,0.7241696,0.00002823248,0.0005628395,0.0007040479,0.00006939234,0.0001816697,0.0009054726],"genre_scores_gemma":[0.9809869,0.00003874465,0.01820482,0.00003214917,0.0004433696,0.00004084048,0.000009464679,0.00004557251,0.0001981118],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7081177,"threshold_uncertainty_score":0.6281363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0342240491220125,"score_gpt":0.2936154988614713,"score_spread":0.2593914497394588,"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."}}