{"id":"W2760375325","doi":"10.1007/978-3-319-61927-9_32","title":"Bearing Fault Feature Extraction Using Autoregressive Coefficients, Linear Discriminant Analysis and Support Vector Machine Under Variable Operating Conditions","year":2017,"lang":"en","type":"book-chapter","venue":"Applied condition monitoring","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Linear discriminant analysis; Autoregressive model; Support vector machine; Pattern recognition (psychology); Artificial intelligence; Feature extraction; Classifier (UML); Computer science; Bearing (navigation); Discriminant; Engineering; Mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002272459,0.0006158436,0.0008165673,0.001070892,0.0001368629,0.0005458861,0.0004458819,0.0004191667,0.001918949],"category_scores_gemma":[0.0005614263,0.0002325919,0.0004569995,0.001236913,0.0001950241,0.0008725089,0.0002295367,0.0005778442,0.001338006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001692872,"about_ca_system_score_gemma":0.0001790065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007033709,"about_ca_topic_score_gemma":0.000856604,"domain_scores_codex":[0.9998525,0.00001162506,0.000009671027,0.00003716073,0.0000771267,0.00001191028],"domain_scores_gemma":[0.9998355,0.00006188647,0.00002216471,0.00001967227,0.00005554042,0.000005252704],"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":[0.00008512845,0.00003834424,0.0004265708,0.0002044171,0.00002437229,0.00006976678,0.00004030002,0.01521261,0.04513984,0.002419325,0.006629324,0.92971],"study_design_scores_gemma":[0.00002202971,0.0002593367,0.01024594,0.00008531969,0.0001056036,0.0006551237,0.00007261319,0.8778074,0.07311804,0.01217818,0.02536554,0.00008477918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02728513,0.004600842,0.9601731,0.0001457372,0.0002453916,0.00004172753,0.0002721804,0.002636306,0.0045995],"genre_scores_gemma":[0.4729287,0.007423912,0.4893289,0.00008826897,0.0004991782,0.0001050143,0.001597027,0.000457541,0.02757141],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001918949,"threshold_uncertainty_score":0.00641948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01887853994166404,"score_gpt":0.3138199816951894,"score_spread":0.2949414417535253,"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."}}