{"id":"W4382658048","doi":"10.1109/tte.2023.3291053","title":"Fault Diagnosis of Electric City Bus High-Voltage Load System Based on Multidomain Sparse Representation","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Transportation Electrification","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Frequency domain; SIGNAL (programming language); Time domain; Fault (geology); Computer science; Voltage; Sparse approximation; Domain (mathematical analysis); Feature (linguistics); Representation (politics); Feature vector; AdaBoost; Pattern recognition (psychology); Algorithm; Artificial intelligence; Support vector machine; Engineering; Mathematics; Computer vision; Electrical engineering","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":[0.0001982095,0.0004969171,0.0004483028,0.0006417913,0.0002202541,0.0003517267,0.0003292547,0.0004083671,0.0005518784],"category_scores_gemma":[0.0007779344,0.0001304087,0.0003075202,0.0003582213,0.0002095296,0.0006281629,0.0003235851,0.0003910646,0.0001672416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002701243,"about_ca_system_score_gemma":0.0002571415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002446003,"about_ca_topic_score_gemma":0.002295054,"domain_scores_codex":[0.9998038,0.0000342515,0.00001155466,0.0000415305,0.00008024627,0.00002859387],"domain_scores_gemma":[0.9997677,0.00005999649,0.00005458155,0.00002268297,0.00008165555,0.00001343405],"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":[0.0005483896,0.0001870449,0.008544348,0.0002032301,0.00009198019,0.0005639797,0.0002117954,0.4917621,0.06627177,0.003895987,0.002657728,0.4250617],"study_design_scores_gemma":[0.000006544993,0.00005551303,0.001515584,0.000004666405,0.00000894144,0.00008669314,0.00002364566,0.9930484,0.004299785,0.0007072142,0.0002372993,0.000005788741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.117421,0.0002324878,0.8799175,0.0002102593,0.00004030169,0.00003986925,0.0000810921,0.0006619118,0.001395629],"genre_scores_gemma":[0.9429439,0.000126417,0.05572278,0.00005013393,0.0000205226,0.00002472464,0.0001671986,0.00001586581,0.0009283521],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002446003,"threshold_uncertainty_score":0.004863501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01525597981227203,"score_gpt":0.2615570520994455,"score_spread":0.2463010722871734,"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."}}