{"id":"W3213931133","doi":"10.1016/j.ymssp.2021.108539","title":"A sparse multivariate time series model-based fault detection method for gearboxes under variable speed condition","year":2021,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; University of Pretoria","keywords":"Fault detection and isolation; Multivariate statistics; Fault (geology); Pattern recognition (psychology); Sparse approximation; Series (stratigraphy); Computer science; Vibration; Engineering; Artificial intelligence; Algorithm; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005196028,0.0002175388,0.000359099,0.0000657122,0.0001795611,0.0002311298,0.00006609297,0.0002085639,0.00002394565],"category_scores_gemma":[0.00006092867,0.0002081557,0.0000579799,0.000175678,0.0000136406,0.0002735672,0.00002427275,0.0001665216,0.000002475925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007632065,"about_ca_system_score_gemma":0.00005204246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005645006,"about_ca_topic_score_gemma":0.000009134723,"domain_scores_codex":[0.998788,0.0000922234,0.0003513968,0.0003275417,0.0001711407,0.0002697173],"domain_scores_gemma":[0.9993941,0.0001524229,0.00007399028,0.0001156131,0.0001678931,0.00009598675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003171808,0.00003728769,0.000001276916,0.000767954,0.00003188827,0.00000343759,0.00002887912,0.2800589,0.7108991,0.001833011,0.00005469157,0.006251816],"study_design_scores_gemma":[0.0003701303,0.00005288514,0.000002377596,0.0002440093,0.00004635874,0.00001816976,0.00002736951,0.7777616,0.2155274,0.00547588,0.0002678286,0.0002059857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005258557,0.0003686907,0.993055,0.00004610503,0.00007916877,0.0004310441,0.00003728534,0.0005534327,0.0001706757],"genre_scores_gemma":[0.8786594,0.000004587712,0.120816,0.00005761541,0.0001068982,0.0001570575,0.0000350494,0.0000538596,0.0001095971],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8734008,"threshold_uncertainty_score":0.8488342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01719996812732811,"score_gpt":0.287285221510159,"score_spread":0.2700852533828309,"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."}}