{"id":"W3088355966","doi":"10.3390/s20195541","title":"Simultaneously Low Rank and Group Sparse Decomposition for Rolling Bearing Fault Diagnosis","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Chongqing Municipal Education Commission; Chongqing Research Program of Basic Research and Frontier Technology; National Natural Science Foundation of China","keywords":"Hankel matrix; Singular value decomposition; Rank (graph theory); Singular value; Fault (geology); Algorithm; Computer science; Feature (linguistics); Bearing (navigation); Pattern recognition (psychology); Matrix (chemical analysis); Matrix decomposition; Feature extraction; Artificial intelligence; Mathematics; Physics","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":[0.0004294903,0.0005046718,0.0004600179,0.0007539076,0.0002188511,0.0004047714,0.0003759215,0.0005728659,0.000957371],"category_scores_gemma":[0.001440315,0.0002065814,0.0004327014,0.0005836892,0.0003493137,0.0006747764,0.0005405667,0.000690402,0.0002880715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002311485,"about_ca_system_score_gemma":0.0006072145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001615568,"about_ca_topic_score_gemma":0.001929861,"domain_scores_codex":[0.9996836,0.00007209258,0.00001604448,0.00004726189,0.0001535585,0.00002732479],"domain_scores_gemma":[0.9995442,0.0001876649,0.00007305793,0.00004156162,0.0001238531,0.00002963982],"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":[0.0002632459,0.00009350175,0.001660316,0.0003289473,0.00006102067,0.0002948105,0.0002354883,0.3712752,0.09057216,0.01898124,0.00482592,0.5114082],"study_design_scores_gemma":[0.000007143552,0.00003581203,0.0002732921,0.000004902304,0.000007746144,0.00006853812,0.00001835418,0.9905351,0.005122448,0.003134007,0.000784071,0.000008609999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009232546,0.0001352688,0.9897826,0.0001091878,0.0000189791,0.00001466359,0.00002729497,0.0001845091,0.0004949733],"genre_scores_gemma":[0.4726709,0.0005308496,0.5240852,0.0001283139,0.0001171107,0.00007686418,0.0002750633,0.00006675005,0.002049012],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001615568,"threshold_uncertainty_score":0.003212333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01235686475524917,"score_gpt":0.2621813543090292,"score_spread":0.24982448955378,"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."}}