{"id":"W4408925380","doi":"10.1016/j.aei.2025.103260","title":"SLDAE: An interpretable stacked Denoising Auto-Encoders for fan fault diagnosis on steelmaking workshops","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Noise reduction; Steelmaking; Fault (geology); Artificial intelligence; Pattern recognition (psychology); Encoder; Computer science; Engineering; Geology; Metallurgy; Seismology; Materials science","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":[0.0005339725,0.0009472183,0.0005501314,0.0006915419,0.000235278,0.000547069,0.00081017,0.0007283606,0.005014922],"category_scores_gemma":[0.00119711,0.0003200495,0.0003973832,0.0003897673,0.0001942213,0.0007084507,0.0007286248,0.0008927036,0.002263909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002600706,"about_ca_system_score_gemma":0.0005855422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003843036,"about_ca_topic_score_gemma":0.009683665,"domain_scores_codex":[0.9997265,0.00004832471,0.00001464659,0.00007489175,0.0001026468,0.00003303155],"domain_scores_gemma":[0.9995659,0.0001304187,0.00002590287,0.00006035248,0.0001919626,0.00002548509],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006203758,0.0001295023,0.001106188,0.0001470111,0.0000651159,0.0001398385,0.00007238139,0.05390694,0.06394524,0.001618054,0.009675667,0.8685737],"study_design_scores_gemma":[0.00002170141,0.00009748516,0.001111142,0.00002402459,0.00002372912,0.0001017466,0.00003629456,0.9432986,0.04790881,0.001585148,0.005768652,0.00002272283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01485432,0.0003773893,0.9768474,0.000105332,0.0001080966,0.00003668981,0.0006382662,0.005756387,0.001276066],"genre_scores_gemma":[0.311462,0.0003787873,0.6754811,0.000226826,0.0000895286,0.0001165049,0.002571851,0.0005034051,0.009170037],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005014922,"threshold_uncertainty_score":0.01677662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005853314295098214,"score_gpt":0.2368531847004298,"score_spread":0.2309998704053316,"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."}}