{"id":"W7082977111","doi":"10.2139/ssrn.5441111","title":"Anomaly Detection of Under-Over Current Faults in Magnetorheological Damper Suspensions using Variational Autoencoders","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Anomaly detection; Interpretability; Autoencoder; Cluster analysis; Pattern recognition (psychology); Robustness (evolution); Multilayer perceptron; Damper; Latent variable","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":[0.0003242528,0.000452817,0.0004361651,0.0005001437,0.0001764421,0.000539869,0.0004626765,0.0007256887,0.0004451807],"category_scores_gemma":[0.001285571,0.0003209191,0.0003038273,0.0002437174,0.0003567648,0.0005360101,0.0004800121,0.0006713415,0.0001072659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003034264,"about_ca_system_score_gemma":0.000235383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003664008,"about_ca_topic_score_gemma":0.003613297,"domain_scores_codex":[0.9999036,0.00001682921,0.000005585311,0.00003279862,0.00002078367,0.00002046877],"domain_scores_gemma":[0.9994583,0.0002913268,0.00008725355,0.00003179063,0.00009524488,0.00003619379],"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":[0.0006155637,0.0001851074,0.01762921,0.0001036502,0.0001048892,0.0002557141,0.000199107,0.7683644,0.05786005,0.002370156,0.0009087592,0.1514034],"study_design_scores_gemma":[0.000001724809,0.000008623241,0.0009457484,0.000001397407,0.000002088011,0.000006391922,0.000006340722,0.9976923,0.0009446829,0.0003562885,0.00003223024,0.000002174742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6075021,0.0002225747,0.3904925,0.0001934176,0.00006775352,0.00001731605,0.00008473238,0.000481204,0.0009383721],"genre_scores_gemma":[0.9896708,0.00003264337,0.009721749,0.00001292158,0.00001449164,0.000004249315,0.00005393688,0.00001499324,0.0004742914],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003664008,"threshold_uncertainty_score":0.007285357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02114827521159299,"score_gpt":0.2759697330794456,"score_spread":0.2548214578678526,"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."}}