{"id":"W2340982871","doi":"","title":"On the use of time synchronous averaging, independent component analysis and support vector machines for bearing fault diagnosis","year":2007,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Crest factor; Generalization; Bearing (navigation); Independent component analysis; Support vector machine; Fault (geology); Computer science; Feature vector; Component (thermodynamics); Envelope (radar); SIGNAL (programming language); Pattern recognition (psychology); Transmission (telecommunications); Principal component analysis; Signal processing; Artificial intelligence; Algorithm; Mathematics; Telecommunications; 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":[0.001100676,0.000840203,0.0006339934,0.0009766039,0.000209184,0.0005181304,0.0004251575,0.0006945129,0.0005353232],"category_scores_gemma":[0.003675136,0.0002009835,0.0004146009,0.0007944435,0.0005531409,0.0009919074,0.0002716507,0.0005191524,0.0003399019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001687869,"about_ca_system_score_gemma":0.0002187523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005407087,"about_ca_topic_score_gemma":0.0006358902,"domain_scores_codex":[0.9995347,0.0001641085,0.00002947223,0.00008440411,0.0001682355,0.00001914423],"domain_scores_gemma":[0.9981388,0.001224139,0.0001561163,0.0001479716,0.0003060103,0.00002694197],"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":[0.0001711084,0.0000882153,0.001876234,0.0002246771,0.00009974325,0.0001129668,0.0000790643,0.09843571,0.02897463,0.01426931,0.0009758144,0.8546926],"study_design_scores_gemma":[0.00001064558,0.0003372452,0.002414258,0.00001938934,0.00004840894,0.0002555254,0.00002214979,0.9675943,0.01469153,0.01169274,0.002874108,0.00003969703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01920476,0.001676914,0.9775808,0.0001209054,0.00005890976,0.00002965516,0.00001783978,0.0002642331,0.00104595],"genre_scores_gemma":[0.4916086,0.003254791,0.5026807,0.0001147265,0.0003117617,0.00007472582,0.00009769647,0.00004570486,0.001811313],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001100676,"threshold_uncertainty_score":0.00582099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02633225402855163,"score_gpt":0.2720000329474613,"score_spread":0.2456677789189096,"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."}}